covid_proj_p1_xrays.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Lewis34cs/corona_audio/blob/main/covid_proj_p1_xrays.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7oaeJQ5N7R_"
      },
      "source": [
        "# Capstone Project Part 1: Chest X-Ray Multi Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gv8QBD0NXStb"
      },
      "source": [
        ">Author: Chris Lewis\r\n",
        "\r\n",
        ">Contact info: lewis34cs@gmail.com\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DJlDc99-sC5"
      },
      "source": [
        ">- Link to [Project repo](https://github.com/Lewis34cs/corona_audio)\r\n",
        "  - Part 1: [`covid_proj_p1_xrays.ipynb`](https://colab.research.google.com/drive/175EDoT7ACYu5JbIgEoFx9DboHzGiWdq2#scrollTo=2DJlDc99-sC5)(This Notebook)\r\n",
        "  - Part 2: [`covid_proj_p2_audio.ipynb`](https://colab.research.google.com/drive/1s4z-GJnNkE2NCx5MVccXxXzvBIc__tTX#scrollTo=WjwyDrj8S80W)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdX9rBGVN-8H"
      },
      "source": [
        "#### COVID-19 Exploration in statistics and diagnosis via imaging and audio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dO6J-MeaONhy"
      },
      "source": [
        ">Since the beginning of 2020, COVID-19 has run rampant throughout the entirety of the globe, resulting in over 100 million cases and 2 million deaths. While vaccines are beginning to be pushed out as our new line of defense, my project's goal is to try and identify other ways to detect COVID-19 in a patient to help slow the spread. In this project, I focus on two different ways to identify COVID-19: multi classification via chest x-rays and binary classification through coughing audio. Both parts will be using Sequential Convolutional Neural Networks. This notebook will focus on COVID-19 diagnosis using chest x-ray images obtained from Kaggle's API.\r\n",
        "\r\n",
        ">To run the Google Colab code, you must allow Google Colab to access an account. I will provide links for the data below. Please be sure to save zipped folders for these audio datasets into your google drive so you can access them on Google Colab. The chest x-ray database was obtained via Kaggle's API.\r\n",
        "\r\n",
        ">**Chest X-ray Database**: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database\r\n",
        "---\r\n",
        ">**Link for functions and zipped audio datasets**: https://drive.google.com/drive/folders/1IQ758RksB0ayUhOohfGh8s25MT4taMsI?usp=sharing\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bTqHVSBE6cnS"
      },
      "source": [
        "Click on the \"add to drive\" button once you've clicked on the Google Drive link above to add the datasets and functions to your Google Drive\r\n",
        "<img src='https://drive.google.com/uc?id=1YYdx9_xhHRsYLqV2X7yUJauG9zKX_LoX'>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3iYW67M_dB6"
      },
      "source": [
        "#### Mounting Google Drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWMevt1_LJI8"
      },
      "source": [
        ">We must run this in order for colab to be able to access our google drive. We can have google colab mount our google drive by either running the cell below or clicking on the folder icon on the left navigation bar and then clicking on the \"Mount Drive\" button at the top of the navigation pane."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tqj7Xn3qHOS7",
        "outputId": "9d1761f6-9a86-44df-eb0b-9855365d9567"
      },
      "source": [
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ho2msxfSS86V"
      },
      "source": [
        "# We need to import these libraries in order to access the shared folder\r\n",
        "import os, sys"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DR1V5RXPKLhh",
        "outputId": "83133a6a-0ecc-4a3b-99e9-1fac3e0bb6b4"
      },
      "source": [
        "# We must install these libraries for our imported functions folder to work\r\n",
        "!pip install split-folders\r\n",
        "!pip install split-folders tqdm\r\n",
        "!pip install pydub\r\n",
        "!pip install soundfile\r\n",
        "!pip install ffmpeg-python\r\n",
        "!pip install zip_files\r\n",
        "!pip install lime\r\n",
        "!pip install tensorflow_addons"
      ],
      "execution_count": 3,
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            "Installing collected packages: lime\n",
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            "Collecting tensorflow_addons\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fc0q1vK2Lfw5"
      },
      "source": [
        ">Set the 'FOLDER' variable as the filepath where the folder containing functions.py and audio datasets are located in your Google Drive."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Juk01kTzTJ6e",
        "outputId": "3b6b1e46-c15c-4713-d07f-19c568387fa7"
      },
      "source": [
        "FOLDER = '/content/drive/MyDrive/chris_lewis_capstone/'\r\n",
        "os.listdir(FOLDER)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['public_dataset.zip',\n",
              " 'virufy_data-main.zip',\n",
              " '__pycache__',\n",
              " 'public_dataset_v2.zip',\n",
              " 'COVID-19_Radiography_Database.zip',\n",
              " 'functions.py']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l0Ewd0U2Lnyx"
      },
      "source": [
        ">Defining a path variable (our FOLDER) to add a specific path for the interpreter to search for and then load the filepath into the extensions in order to import all the functions from the functions.py file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjwyDrj8S80W"
      },
      "source": [
        "sys.path.append(FOLDER)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rkr-WjJ2TiAz"
      },
      "source": [
        "%load_ext autoreload\r\n",
        "%autoreload 2\r\n",
        "from functions import *"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OIQJyJIdTKz"
      },
      "source": [
        "#### Importing Libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H22zeCeIL751"
      },
      "source": [
        ">These are the libraries we used in the project. Note that it is not necessary to run these cells because the functions.py file has already imported the libraries for us."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-euk8zDHjRj"
      },
      "source": [
        "#Setting seeds first in order to achieve better consistency in scoring\r\n",
        "import numpy as np\r\n",
        "import tensorflow as tf\r\n",
        "\r\n",
        "np.random.seed(321)\r\n",
        "tf.random.set_seed(321)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJrDwLTlFnA9"
      },
      "source": [
        "import pandas as pd\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import matplotlib.image as mpimg\r\n",
        "%matplotlib inline\r\n",
        "import os, sys, glob, shutil"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpotBbfoHnPZ"
      },
      "source": [
        "from sklearn import metrics\r\n",
        "from sklearn.dummy import DummyClassifier\r\n",
        "from sklearn.utils import class_weight\r\n",
        "from skimage.segmentation import mark_boundaries\r\n",
        "import time\r\n",
        "from IPython.display import Image, display\r\n",
        "from PIL import Image as Im\r\n",
        "import cv2\r\n",
        "import shutil\r\n",
        "from google.colab import files\r\n",
        "\r\n",
        "# tensorflow libraries\r\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\r\n",
        "from tensorflow.keras.preprocessing.image import array_to_img, img_to_array, load_img\r\n",
        "from tensorflow.keras.models import Sequential, save_model, load_model\r\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\r\n",
        "from tensorflow.keras import models, layers, optimizers, regularizers\r\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uO52T4NkKN3c"
      },
      "source": [
        "import splitfolders\r\n",
        "import lime\r\n",
        "from lime import lime_image\r\n",
        "from zipfile import ZipFile"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYe0h27VDaKI"
      },
      "source": [
        "# setting plot background style\r\n",
        "plt.style.use('dark_background')"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SkNTjfsKerhc"
      },
      "source": [
        "# PART 1: Classifying COVID-19 Chest X-ray Images via Sequential Convolutional Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "BpNGluXDHt9N",
        "outputId": "e231cb39-6542-410f-ab02-1606d60f3082"
      },
      "source": [
        "# Viewing where we are in the directory\r\n",
        "os.path.abspath(os.curdir)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwWBJSFweJ9h"
      },
      "source": [
        "## Obtaining COVID CXR dataset via Kaggle API"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLalmBaSB0yu"
      },
      "source": [
        ">In order to obtain the chest x-ray database from Kaggle, we make a call to Kaggle's API using an API key. This database still updates regularly, which means that more images are placed into each class, giving us more data to train off of. Instead of downloading the file manually to our google drive, we prefer this route in order to avoid having to constantly check for database updates. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EytsKXluWNku"
      },
      "source": [
        ">The dataset for this part can be found on Kaggle:\r\n",
        "-  Chest X-ray database: \r\n",
        "  - https://www.kaggle.com/tawsifurrahman/covid19-radiography-database. \r\n",
        "- For instructions on how to get an API key: https://www.kaggle.com/docs/api\r\n",
        "\r\n",
        ">Once you have the API key, please upload it in the cell below.\r\n",
        "The below cells are retrieved from Kaggle."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eciGnyx3LwAc"
      },
      "source": [
        "**UPDATE** On March 6th 2021, the Kaggle dataset used in this project was updated - this included more images and a new class: lung_opacity. As of right now, this new class has not yet been included in the majority of the below code. Instead of using the Kaggle API to access the database's current version, we will instead be using the zipped file of the dataset that was used during the making of this project. In the future, I will provide an updated version of this part of the project in the github repository."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLiPTniJ8rqQ"
      },
      "source": [
        "#### Unzipping Folder Contents"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwjQBaONNTDF"
      },
      "source": [
        ">Unzipping the file and extracting to the root directory. If you chose to directly download the database from the kaggle page instead of making a call to the API, replace the 'zipped_file' variable with the location of the zipped file in your Google Drive. Do not unzip the zipped folder before loading into Google Colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k69IMKaj4TK-",
        "outputId": "6d2122bf-d9f1-4c38-9723-f6944578f948"
      },
      "source": [
        "# Making sure we are in the root directory\r\n",
        "%cd ~\r\n",
        "%cd .. \r\n",
        "\r\n",
        "zipped_file = FOLDER + \"COVID-19_Radiography_Database.zip\"\r\n",
        "\r\n",
        "with ZipFile(zipped_file, 'r') as zipf:\r\n",
        "      zipf.extractall()\r\n",
        "      print('Done')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/root\n",
            "/\n",
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7sd-z-7l1wq4"
      },
      "source": [
        "# setting a variable called cxr_base_folder that contains the directory of where \r\n",
        "# our chest x-ray images are\r\n",
        "cxr_base_folder = ensure_filepath('/COVID-19 Radiography Database')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "cAtQb-qE0-2n",
        "outputId": "31464551-d6e1-4792-87d0-dc071ded23c8"
      },
      "source": [
        "# Viewing the contents within our cxr_base_folder\r\n",
        "folder_contents(cxr_base_folder)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Contents of /COVID-19 Radiography Database/:\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "['COVID',\n",
              " 'COVID.metadata.xlsx',\n",
              " 'NORMAL',\n",
              " 'NORMAL.metadata.xlsx',\n",
              " 'README.md.txt',\n",
              " 'Viral Pneumonia',\n",
              " 'Viral Pneumonia.metadata.xlsx']"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rc_EzJED1JCS"
      },
      "source": [
        "#### Exploring the metadata for each class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jJEKaU90WjZ"
      },
      "source": [
        "covid_df = pd.read_excel(cxr_base_folder+'COVID.metadata.xlsx')"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "-zXJVhZB0jC9",
        "outputId": "3059654a-3116-45c2-a1a5-e844b0017f1c"
      },
      "source": [
        "covid_df.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FILE NAME</th>\n",
              "      <th>FORMAT</th>\n",
              "      <th>SIZE</th>\n",
              "      <th>URL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>COVID 1</td>\n",
              "      <td>PNG</td>\n",
              "      <td>256*256</td>\n",
              "      <td>https://sirm.org/category/senza-categoria/covi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>COVID 2</td>\n",
              "      <td>PNG</td>\n",
              "      <td>256*256</td>\n",
              "      <td>https://sirm.org/category/senza-categoria/covi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>COVID 3</td>\n",
              "      <td>PNG</td>\n",
              "      <td>256*256</td>\n",
              "      <td>https://sirm.org/category/senza-categoria/covi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>COVID 4</td>\n",
              "      <td>PNG</td>\n",
              "      <td>256*256</td>\n",
              "      <td>https://sirm.org/category/senza-categoria/covi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>COVID 5</td>\n",
              "      <td>PNG</td>\n",
              "      <td>256*256</td>\n",
              "      <td>https://sirm.org/category/senza-categoria/covi...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  FILE NAME FORMAT     SIZE                                                URL\n",
              "0   COVID 1    PNG  256*256  https://sirm.org/category/senza-categoria/covi...\n",
              "1   COVID 2    PNG  256*256  https://sirm.org/category/senza-categoria/covi...\n",
              "2   COVID 3    PNG  256*256  https://sirm.org/category/senza-categoria/covi...\n",
              "3   COVID 4    PNG  256*256  https://sirm.org/category/senza-categoria/covi...\n",
              "4   COVID 5    PNG  256*256  https://sirm.org/category/senza-categoria/covi..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7GR_GOm0n2X"
      },
      "source": [
        "normal_df = pd.read_excel(cxr_base_folder+'NORMAL.metadata.xlsx')"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "QhychOFe04Ev",
        "outputId": "44e5fb50-b80a-4f16-9820-d34501d94d91"
      },
      "source": [
        "normal_df.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FILE NAME</th>\n",
              "      <th>FORMAT</th>\n",
              "      <th>SIZE</th>\n",
              "      <th>URL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NORMAL-1</td>\n",
              "      <td>PNG</td>\n",
              "      <td>1024*1024</td>\n",
              "      <td>https://www.kaggle.com/paultimothymooney/chest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NORMAL-2</td>\n",
              "      <td>PNG</td>\n",
              "      <td>1024*1024</td>\n",
              "      <td>https://www.kaggle.com/paultimothymooney/chest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NORMAL-3</td>\n",
              "      <td>PNG</td>\n",
              "      <td>1024*1024</td>\n",
              "      <td>https://www.kaggle.com/paultimothymooney/chest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NORMAL-4</td>\n",
              "      <td>PNG</td>\n",
              "      <td>1024*1024</td>\n",
              "      <td>https://www.kaggle.com/paultimothymooney/chest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NORMAL-5</td>\n",
              "      <td>PNG</td>\n",
              "      <td>1024*1024</td>\n",
              "      <td>https://www.kaggle.com/paultimothymooney/chest...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  FILE NAME  ...                                                URL\n",
              "0  NORMAL-1  ...  https://www.kaggle.com/paultimothymooney/chest...\n",
              "1  NORMAL-2  ...  https://www.kaggle.com/paultimothymooney/chest...\n",
              "2  NORMAL-3  ...  https://www.kaggle.com/paultimothymooney/chest...\n",
              "3  NORMAL-4  ...  https://www.kaggle.com/paultimothymooney/chest...\n",
              "4  NORMAL-5  ...  https://www.kaggle.com/paultimothymooney/chest...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipMPCBlG06VT"
      },
      "source": [
        "viral_pneumo_df = pd.read_excel(cxr_base_folder+'Viral Pneumonia.metadata.xlsx')"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "OFoXfcQq1FgN",
        "outputId": "8ea7eaab-b850-4175-edf0-b59394a3ac29"
      },
      "source": [
        "viral_pneumo_df.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FILE NAME</th>\n",
              "      <th>FORMAT</th>\n",
              "      <th>SIZE</th>\n",
              "      <th>URL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Viral Pneumonia-1</td>\n",
              "      <td>PNG</td>\n",
              "      <td>1024*1024</td>\n",
              "      <td>https://www.kaggle.com/paultimothymooney/chest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Viral Pneumonia-2</td>\n",
              "      <td>PNG</td>\n",
              "      <td>1024*1024</td>\n",
              "      <td>https://www.kaggle.com/paultimothymooney/chest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Viral Pneumonia-3</td>\n",
              "      <td>PNG</td>\n",
              "      <td>1024*1024</td>\n",
              "      <td>https://www.kaggle.com/paultimothymooney/chest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Viral Pneumonia-4</td>\n",
              "      <td>PNG</td>\n",
              "      <td>1024*1024</td>\n",
              "      <td>https://www.kaggle.com/paultimothymooney/chest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Viral Pneumonia-5</td>\n",
              "      <td>PNG</td>\n",
              "      <td>1024*1024</td>\n",
              "      <td>https://www.kaggle.com/paultimothymooney/chest...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           FILE NAME  ...                                                URL\n",
              "0  Viral Pneumonia-1  ...  https://www.kaggle.com/paultimothymooney/chest...\n",
              "1  Viral Pneumonia-2  ...  https://www.kaggle.com/paultimothymooney/chest...\n",
              "2  Viral Pneumonia-3  ...  https://www.kaggle.com/paultimothymooney/chest...\n",
              "3  Viral Pneumonia-4  ...  https://www.kaggle.com/paultimothymooney/chest...\n",
              "4  Viral Pneumonia-5  ...  https://www.kaggle.com/paultimothymooney/chest...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWJhpAJq20yI"
      },
      "source": [
        "covid_df.name = \"COVID-19\"\r\n",
        "normal_df.name = \"Normal\"\r\n",
        "viral_pneumo_df.name = \"Pneumonia\""
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5veZE_P2ENi",
        "outputId": "85e2e3a2-a983-4f27-dab9-1e8c5f543a87"
      },
      "source": [
        "for df in [covid_df, normal_df, viral_pneumo_df]:\r\n",
        "  print(f\"Unique sizes for {df.name}: {df['SIZE'].unique()}\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unique sizes for COVID-19: ['256*256']\n",
            "Unique sizes for Normal: ['1024*1024']\n",
            "Unique sizes for Pneumonia: ['1024*1024']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LSlFKR9M1Ncb"
      },
      "source": [
        "We do not receive much information about the patients within each of the classes. Each dataframe contains 4 columns: File name, Format, Size, and the URL. There is no demographic information provided - however, we notice that the sizes are not all the same. While the Normal and Viral Pneumonia classes both contain 1024x1024 images, our COVID class only contains 256x256 images. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJjN9JiVe8AJ"
      },
      "source": [
        "#### Identifying the number of images in our dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PaXdBPyx4qbg",
        "outputId": "2eb3df37-0b6b-4680-97f6-67505f1e4cd8"
      },
      "source": [
        "total_image_number = num_items(cxr_base_folder)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/COVID-19 Radiography Database/\n",
            "Number of items in COVID: 1200\n",
            "Number of items in NORMAL: 1341\n",
            "Number of items in Viral Pneumonia: 1345\n",
            "Total number of items: 3886\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p7jHjNyf9MSD"
      },
      "source": [
        ">From here, we view the extracted contents and count the number of images per class along with the total image count. We see that we are working with 3800+ images."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8TsOQCWgfCDk"
      },
      "source": [
        "### Splitting our images into train, test, and validation sets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5hpVo3sOLQP"
      },
      "source": [
        ">Here, we are making a folder to store our train, test, and validation folders. If you would like to store the folders in another filepath, please specify where you would like to store your train, test, and validation folders in the 'cxr_folders' variable. Note that this folder is temporary and will be deleted later on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4ZtlCYyb3_E"
      },
      "source": [
        "cxr_folders = ensure_filepath('/content/cxr_folders')\r\n",
        "\r\n",
        "os.makedirs(cxr_folders, exist_ok=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-fPEoz5s9TPc"
      },
      "source": [
        "#### Temporary Folder Creation & Check"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ze3fL6EtOfRG"
      },
      "source": [
        ">The splitfolders library is extremely effective and very easy to use. It allows us to feed in a directory address (base_folder variable) and give it a place to put the new train, test, and validation folders (output variable). Note that the output address must already be existing. We also set a seed in order to help with model score reproducibility when training our models. The ratio parameter required floats that determines the percentage of data going to each folder.\r\n",
        "- For more info about the splitfolders library, click here: https://github.com/jfilter/split-folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QXJXVHTv8Tgt",
        "outputId": "abc2da44-e9cc-44e4-8d28-864fb4821ac2"
      },
      "source": [
        "splitfolders.ratio(input=cxr_base_folder, \r\n",
        "                    output=cxr_folders, \r\n",
        "                    seed=42, ratio=(0.7, 0.1, 0.2)) # ratio order: train, val, test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Copying files: 3886 files [00:07, 532.09 files/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6SUIl7VXB96M"
      },
      "source": [
        "## Recommendation:\r\n",
        "\r\n",
        ">If you prefer working with folder structures when dealing with training and testing data, I would highly recommend using the splitfolders library. Through the use of this library, I was able to create train, test, and validation folders, create image data generators, and have those generators access the data by flowing the data from my directories. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ivWqCPpszaa",
        "outputId": "364072fd-5b99-40ca-cd82-633a825e35c0"
      },
      "source": [
        "folder_check(new_dir=cxr_folders, orig_dir=cxr_base_folder, check=1, )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/cxr_folders/train/\n",
            "Number of items in Viral Pneumonia: 941\n",
            "Number of items in COVID: 840\n",
            "Number of items in NORMAL: 938\n",
            "Total number of items: 2719\n",
            "\n",
            "/content/cxr_folders/test/\n",
            "Number of items in Viral Pneumonia: 270\n",
            "Number of items in COVID: 240\n",
            "Number of items in NORMAL: 269\n",
            "Total number of items: 779\n",
            "\n",
            "/content/cxr_folders/val/\n",
            "Number of items in Viral Pneumonia: 134\n",
            "Number of items in COVID: 120\n",
            "Number of items in NORMAL: 134\n",
            "Total number of items: 388\n",
            "\n",
            "/COVID-19 Radiography Database/\n",
            "Number of items in Viral Pneumonia: 1345\n",
            "Number of items in COVID: 1200\n",
            "Number of items in NORMAL: 1341\n",
            "Total number of items: 3886\n",
            "\n",
            "Are the image numbers equal?\n",
            "True\n",
            "\n",
            "Total image count: 3886\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ez6f3Iw_AT_2"
      },
      "source": [
        "# Creating variables to hold our addresses for our train, test, and val folders\r\n",
        "train_folder, test_folder, val_folder = create_ttv(cxr_folders)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "lwrNlYjAWuyT",
        "outputId": "6ba81847-4445-49bd-e75b-015dd005a308"
      },
      "source": [
        "train_folder"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/cxr_folders/train/'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFh0icmmQDEZ"
      },
      "source": [
        ">Since we are using a DummyClassifier as our baseline model, it cannot work with ImageDataGenerators. So instead, we will create X and y train, test, and validation variables by feeding in a batch size equal to the amount of data in each train, test, and validation folder."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHcxc1V5BP1Z",
        "outputId": "dbb00148-b64b-4413-99a2-bbb4fd0b4db5"
      },
      "source": [
        "TRAIN_BATCH_SIZE, TEST_BATCH_SIZE, VAL_BATCH_SIZE = batch_sizes([train_folder, \r\n",
        "                                                                 test_folder, \r\n",
        "                                                                 val_folder])\r\n",
        "print(f\"Train batch size: {TRAIN_BATCH_SIZE}\\nTest batch size: {TEST_BATCH_SIZE}\"\r\n",
        "      f\"\\nValidation batch size: {VAL_BATCH_SIZE}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train batch size: 2719\n",
            "Test batch size: 779\n",
            "Validation batch size: 388\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eSVWxYe8ezSR"
      },
      "source": [
        "#### Setting up ImageDataGenerators"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7_jF2BZQmaH"
      },
      "source": [
        ">Setting up our variables for the dummy classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2MV1_PofrEG"
      },
      "source": [
        "# Defining our image size\r\n",
        "IMG_SIZE = (128, 128)\r\n",
        "\r\n",
        "# Creating ImageDataGenerators for train and test/val\r\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\r\n",
        "test_val_datagen = ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVTrA1ongY9X",
        "outputId": "ac4607db-5aef-49d2-d158-e9ecdecb10b8"
      },
      "source": [
        "dummy_train_generator = train_datagen.flow_from_directory(train_folder, \r\n",
        "                                                   target_size=IMG_SIZE,\r\n",
        "                                                   batch_size=TRAIN_BATCH_SIZE,\r\n",
        "                                                   class_mode='categorical')\r\n",
        "\r\n",
        "dummy_test_generator = test_val_datagen.flow_from_directory(test_folder, \r\n",
        "                                                     target_size=IMG_SIZE,\r\n",
        "                                                     batch_size=TEST_BATCH_SIZE,\r\n",
        "                                                     class_mode='categorical')\r\n",
        "\r\n",
        "dummy_val_generator = test_val_datagen.flow_from_directory(val_folder, \r\n",
        "                                                    target_size=IMG_SIZE,\r\n",
        "                                                    batch_size=VAL_BATCH_SIZE,\r\n",
        "                                                    class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2719 images belonging to 3 classes.\n",
            "Found 779 images belonging to 3 classes.\n",
            "Found 388 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-l8Obdqhsiv",
        "outputId": "f9c3b244-cc9e-43ff-bae1-a6295ffa9b56"
      },
      "source": [
        "# Viewing the classes within our iterator\r\n",
        "dummy_train_generator.class_indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'COVID': 0, 'NORMAL': 1, 'Viral Pneumonia': 2}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTH7GcjnQtQL"
      },
      "source": [
        ">Here is where we create our X and y variables for each train, test, and validation set. Notice that by calling next() on each iterator, we are feeding X and y variables data and labels equivalent to the batch sizes of the respective entire folder. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZAg2IW0hv2X",
        "outputId": "47c6d57e-d1c0-4402-9dc1-6d2dd2a224d7"
      },
      "source": [
        "start = time.time()\r\n",
        "# Creating X and y values for dummy train, test, and validation sets\r\n",
        "X_train, y_train = next(dummy_train_generator)\r\n",
        "X_test, y_test = next(dummy_test_generator)\r\n",
        "X_val, y_val = next(dummy_val_generator)\r\n",
        "\r\n",
        "end = time.time()\r\n",
        "time_count(start, end)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time to run cell: 48 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "Svd7sZlpNdkY",
        "outputId": "a202a4b7-fecc-4f5e-c68c-ab6747ecd494"
      },
      "source": [
        "random_image(X_train, np.argmax(y_train, axis=1), verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NORMAL : Class 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7F4F80218150>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0bv6zgldhCoO"
      },
      "source": [
        "#### Baseline Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQ4mcWMoREWt"
      },
      "source": [
        ">We need to one-hot encode our classes from our generated labels in order for the confusion matrix to be able to understand the we are working with more than two classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehWzrZxCiASj"
      },
      "source": [
        "# Creating and fitting our dummy classifier\r\n",
        "dummy = DummyClassifier(strategy='stratified')\r\n",
        "dummy.fit(X_train, y_train)\r\n",
        "\r\n",
        "# Creating our y_pred variable\r\n",
        "y_pred = dummy.predict(X_test)\r\n",
        "\r\n",
        "y_pred = np.argmax(y_pred, axis=1)\r\n",
        "y_test_ohe = np.argmax(y_test, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "-eHnzUGInXWA",
        "outputId": "e41861e3-cd48-4144-d0cf-184786909f12"
      },
      "source": [
        "#Printing classification report and plotting confusion matrix\r\n",
        "print(metrics.classification_report(y_test_ohe, y_pred));\r\n",
        "\r\n",
        "plt.figure(figsize=(7, 6))\r\n",
        "cm = metrics.confusion_matrix(y_test_ohe, y_pred, labels = [0, 1, 2], \r\n",
        "                              normalize='true')\r\n",
        "sns.heatmap(cm, cmap=\"Greens\", annot=True, square=True)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.32      0.64      0.42       240\n",
            "           1       0.36      0.26      0.30       269\n",
            "           2       0.38      0.14      0.21       270\n",
            "\n",
            "    accuracy                           0.34       779\n",
            "   macro avg       0.35      0.35      0.31       779\n",
            "weighted avg       0.35      0.34      0.31       779\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjGVMJwenHVe"
      },
      "source": [
        ">We built a baseline dummy classifier model that has an accuracy of 34%, and it tends to guess for the class == 0, which is our Covid class. Since our dataset is relatively balanced in terms of classes, we expect to see the accuracy be around 33%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bcU-41f9BLIE"
      },
      "source": [
        "## First CNN Model for CXRs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xlVOAhRAnQL"
      },
      "source": [
        ">Now we will create new generators that we will feed into a CNN model. Notice for the test generator we must set the parameter shuffle=False. This is because when we want to evaluate the model, we want be able to see the model's actual score against the test generator. If we were to set shuffle=True for the test generator, the labels in the test generator would not line up with the images when trying to evaluate the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x88yIEVOuFEI",
        "outputId": "d1b64f79-fa7c-42f2-b0c4-5e0c0c9656fb"
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(train_folder, \r\n",
        "                                                   target_size=IMG_SIZE,\r\n",
        "                                                   batch_size=32,\r\n",
        "                                                   class_mode='categorical')\r\n",
        "\r\n",
        "test_generator = test_val_datagen.flow_from_directory(test_folder, \r\n",
        "                                                     target_size=IMG_SIZE,\r\n",
        "                                                     batch_size=32,\r\n",
        "                                                     class_mode='categorical', \r\n",
        "                                                     shuffle=False)\r\n",
        "\r\n",
        "val_generator = test_val_datagen.flow_from_directory(val_folder, \r\n",
        "                                                    target_size=IMG_SIZE,\r\n",
        "                                                    batch_size=32,\r\n",
        "                                                    class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2719 images belonging to 3 classes.\n",
            "Found 779 images belonging to 3 classes.\n",
            "Found 388 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VdMavfRbHcRG"
      },
      "source": [
        "# Defining input shape to feed into our CNN\r\n",
        "INPUT_SHAPE = train_generator.image_shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14Xca2Jko8CN"
      },
      "source": [
        "#### Viewing our Class Weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D1_pUY31RXws"
      },
      "source": [
        ">We will now create a dictionary containing the weights for each class by using our custom make_class_weights() function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3P5I5uYJUpAu",
        "outputId": "dfb82694-1d56-4729-a7e4-cac23e7bf6ae"
      },
      "source": [
        "class_weights_dict = make_class_weights(train_generator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{0: 1.078968253968254, 1: 0.9662402274342573, 2: 0.963159759121502}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJ__nNrnI8-y"
      },
      "source": [
        "### Creating CNN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhc68bQjGAhm"
      },
      "source": [
        "def create_basic_cnn(shape='',\r\n",
        "                     metrics=['acc', tf.keras.metrics.Precision(name='precision'),\r\n",
        "                              tf.keras.metrics.Recall(name='recall'), \r\n",
        "                              tf.keras.metrics.AUC(name='auc')]):\r\n",
        "  \"\"\"\r\n",
        "  Definition:\r\n",
        "  Creates a basic cnn model consisting of three layers and an output layer.\r\n",
        "\r\n",
        "  Args:\r\n",
        "  shape: this function requires an input shape in order for the model to be created.\r\n",
        "\r\n",
        "  Returns:\r\n",
        "  Returns a compiled model.\r\n",
        "  \"\"\"\r\n",
        "  # We are requiring an input shape since we print the model.summary() at the \r\n",
        "  # end of the function\r\n",
        "  if len(shape) < 1:\r\n",
        "    print('Please define input shape!')\r\n",
        "  else:\r\n",
        "    # Input layer\r\n",
        "    model = Sequential()\r\n",
        "    model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=shape))\r\n",
        "    model.add(MaxPooling2D((2, 2)))\r\n",
        "\r\n",
        "    # Second layer\r\n",
        "    model.add(layers.Conv2D(64, (3,3), activation='relu'))\r\n",
        "    model.add(MaxPooling2D((2, 2)))\r\n",
        "      \r\n",
        "    # Third layer\r\n",
        "    model.add(Flatten())\r\n",
        "    model.add(layers.Dense(128, activation='relu'))\r\n",
        "      \r\n",
        "    # Output layer\r\n",
        "    model.add(layers.Dense(3, activation='softmax'))\r\n",
        "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=metrics)\r\n",
        "    print(model.summary())\r\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1SSJiQqIUxU",
        "outputId": "c25a50ac-5231-4ea4-9ef6-57ca64c5b41b"
      },
      "source": [
        "basic_model = create_basic_cnn(shape=INPUT_SHAPE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 126, 126, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 63, 63, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 61, 61, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 57600)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               7372928   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 387       \n",
            "=================================================================\n",
            "Total params: 7,392,707\n",
            "Trainable params: 7,392,707\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GGLJPDe6M5_"
      },
      "source": [
        "### Fitting model, Plotting scores, Reporting evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPzu7F75Rj9d"
      },
      "source": [
        ">The fit_plot_report_gen() function takes in a CNN model and fits it on a training generator. After each epoch the validation generator is evaluated, which allows us to see if the model is learning or overfitting. Once the model finishes training, we evaluate the test generator and create a classification report, confusion matrix, and a plot for each metric that was scored over the epochs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rw2hdWCAdhg9",
        "outputId": "7d197d4a-bcb8-4af7-877b-5f0ca7a4d630"
      },
      "source": [
        "hist1 = fit_plot_report_gen(basic_model, train_generator, test_generator, val_generator, \r\n",
        "                class_weights=class_weights_dict, epochs=5, batch_size=32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "85/85 [==============================] - 54s 445ms/step - loss: 1.0916 - acc: 0.6628 - precision: 0.7147 - recall: 0.4871 - auc: 0.8032 - val_loss: 0.1802 - val_acc: 0.9304 - val_precision: 0.9304 - val_recall: 0.9304 - val_auc: 0.9918\n",
            "Epoch 2/5\n",
            "85/85 [==============================] - 36s 427ms/step - loss: 0.1562 - acc: 0.9372 - precision: 0.9386 - recall: 0.9332 - auc: 0.9928 - val_loss: 0.1108 - val_acc: 0.9639 - val_precision: 0.9638 - val_recall: 0.9613 - val_auc: 0.9960\n",
            "Epoch 3/5\n",
            "85/85 [==============================] - 35s 418ms/step - loss: 0.1014 - acc: 0.9656 - precision: 0.9657 - recall: 0.9635 - auc: 0.9966 - val_loss: 0.1200 - val_acc: 0.9639 - val_precision: 0.9662 - val_recall: 0.9588 - val_auc: 0.9956\n",
            "Epoch 4/5\n",
            "85/85 [==============================] - 36s 419ms/step - loss: 0.0807 - acc: 0.9697 - precision: 0.9708 - recall: 0.9690 - auc: 0.9979 - val_loss: 0.1161 - val_acc: 0.9716 - val_precision: 0.9742 - val_recall: 0.9716 - val_auc: 0.9935\n",
            "Epoch 5/5\n",
            "85/85 [==============================] - 36s 420ms/step - loss: 0.0556 - acc: 0.9815 - precision: 0.9815 - recall: 0.9814 - auc: 0.9990 - val_loss: 0.1446 - val_acc: 0.9588 - val_precision: 0.9588 - val_recall: 0.9588 - val_auc: 0.9931\n",
            "---------------------------------------------------------\n",
            "                  Classification Report\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97       240\n",
            "           1       0.96      0.89      0.93       269\n",
            "           2       0.89      0.97      0.93       270\n",
            "\n",
            "    accuracy                           0.94       779\n",
            "   macro avg       0.94      0.94      0.94       779\n",
            "weighted avg       0.94      0.94      0.94       779\n",
            "\n",
            "---------------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h52loOKrf/OaXeuyxp7RixSr9/e+vnHTbA7EnDADok6vOnqerzp7X7R0MCzev0P6jlSHjRztHatlFd5zUNmfOnK1Vqx7V1Vcv0Jtvvq7bbrtDL7zwXMRnC3eVkpLS8WCHRNXW1urw4fB77R9++H8688xxSk5OUXJyipYvX3FS7e6KPWEAQFR9YVxu2PFzx84+6XWeddYEHTzoUVVVpY4cOaING17T8OFu/eIXv9LSpXf1ah2VlQdUXPy8HnlkpVatekrZ2dmSFPaZxAkJibLttpNub3cIYQBAVE0dcaFuzPmqRjtHKsFK0GjnSN2Y89U+X5Q1bdoMPfXU4/rc52aprq5Wo0ePkdT5bOFIamtrlZmZqbS0NP373++rsrJSzc3Ncjqd8nq9kqR3331HkjR27Dh99FGFGhoadOzYMd1++2L1x0MIORwNAIi6qSMu7PcroWfNmq1bb71Jv/nNajU1NYY8W3jdupd7XP6cc85VamqavvWtm3TBBRfqyiuv0iOPPKj/+q/lWrToG3rvvXJdeOEUSVJqaqoWLbpVt9++WJK0cOFXg/aYT1ZMPk84UDw9RYlaBqd4qSVe6pCoZbCKl1p4njAAAP3kzTdf1wsvPB8y/stf/opmzTr589L9gRAGAMS1GTNmacaMWaabERYXZgEAYAghDACAIYQwAACGEMIAABjSqwuzioqKtH37dlmWpWXLlmny5Mn+ac8//7xefvllJSQk6Pzzz9fdd98dtcYCABBPIu4Jb9myRRUVFSouLlZhYaEKCwv90+rr6/WrX/1Kzz//vFavXq09e/bonXfeiWqDAQCIFxFDuKysTHl5eZKkCRMmqK6uTvX17U+ZGDJkiIYMGaKGhga1tLSosbFRGRkZ0W0xAABxIuLhaK/Xq5ycHP9wVlaWPB6PnE6nkpOTtWTJEuXl5Sk5OVlXXHGFxo8f3+P6MjPT5HAk9r3lAbq7E0ksopbBKV5qiZc6JGoZrOKlloGq44Rv1hF4l8v6+no9+eSTeuWVV+R0OnX99dfr/fff18SJE7tdvqam4eRa2o14uU2aRC2DVbzUEi91SNQyWMVLLQN528qIh6Pdbrf/aRKSVF1dLZfLJUnas2ePzjjjDGVlZSkpKUlTp07Vjh07+qnJAADEt4ghPH36dJWWlkqSysvL5Xa75XQ6JUmjR4/Wnj171NTUJEnasWOHxo0bF73WAgAQRyIejp4yZYpycnKUn58vy7JUUFCgkpISpaena86cOVq0aJGuu+46JSYm6pOf/KSmTp06EO0GACDm9eqc8NKlS4OGA8/55ufnKz8/v39bBQDAKYA7ZgEAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIY7ezFRUVKTt27fLsiwtW7ZMkydP9k87cOCAvvvd76q5uVmTJk3Sj3/846g1FgCAeBJxT3jLli2qqKhQcXGxCgsLVVhYGDR9+fLluummm7RmzRolJiZq//79UWssAADxJGIIl5WVKS8vT5I0YcIE1dXVqb6+XpLU1tamt99+W7m5uZKkgoICjRo1KorNBQAgfkQ8HO31epWTk+MfzsrKksfjkdPp1KFDhzR06FD99Kc/VXl5uaZOnao777yzx/VlZqbJ4Ujse8sDuFzp/bo+k6hlcIqXWuKlDolaBqt4qWWg6ujVOeFAtm0Hva+qqtJ1112n0aNH65ZbbtFrr72mSy65pNvla2oaTqqh3XG50uXxHOnXdZpCLYNTvNQSL3VI1DJYxUst0aiju1CPeDja7XbL6/X6h6urq+VyuSRJmZmZGjVqlM4880wlJiZq2rRp+uCDD/qpyQAAxLeIITx9+nSVlpZKksrLy+V2u+V0OiVJDodDZ5xxhj788EP/9PHjx0evtQAAxJGIh6OnTJminJwc5efny7IsFRQUqKSkROnp6ZozZ46WLVumu+66S7Zt69xzz/VfpAUAAHrWq3PCS5cuDRqeOHGi//3YsWO1evXq/m0VAACnAO6YBQCAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABjSqxAuKirSwoULlZ+fr3fffTfsPI888oi+/vWv92vjAACIZxFDeMuWLaqoqFBxcbEKCwtVWFgYMs/u3bu1devWqDQQAIB4FTGEy8rKlJeXJ0maMGGC6urqVF9fHzTP8uXLdccdd0SnhQAAxClHpBm8Xq9ycnL8w1lZWfJ4PHI6nZKkkpISXXTRRRo9enSvNpiZmSaHI/Ekmxuey5Xer+sziVoGp3ipJV7qkKhlsIqXWgaqjogh3JVt2/73tbW1Kikp0a9//WtVVVX1avmamoYT3WSPXK50eTxH+nWdplDL4BQvtcRLHRK1DFbxUks06ugu1CMejna73fJ6vf7h6upquVwuSdJbb72lQ4cO6Wtf+5puu+02lZeXq6ioqJ+aDABAfIsYwtOnT1dpaakkqby8XG63238o+tJLL9Vf//pXvfjii1q1apVycnK0bNmy6LYYAIA4EfFw9JQpU5STk6P8/HxZlqWCggKVlJQoPT1dc+bMGYg2AgAQl3p1Tnjp0qVBwxMnTgyZZ8yYMXruuef6p1UAAJwCuGMWAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABji6M1MRUVF2r59uyzL0rJlyzR58mT/tLfeeksrVqxQQkKCxo8fr8LCQiUkkO0AAEQSMS23bNmiiooKFRcXq7CwUIWFhUHT77vvPv385z/XCy+8oKNHj2rDhg1RaywAAPEkYgiXlZUpLy9PkjRhwgTV1dWpvr7eP72kpETZ2dmSpKysLNXU1ESpqQAAxJeIh6O9Xq9ycnL8w1lZWfJ4PHI6nZLkf62urtbGjRv1ne98p8f1ZWamyeFI7EubQ7hc6f26PpOoZXCKl1ripQ6JWgareKlloOro1TnhQLZth4w7ePCgbr31VhUUFCgzM7PH5WtqGk50kz1yudLl8Rzp13WaQi2DU7zUEi91SNQyWMVLLdGoo7tQj3g42u12y+v1+oerq6vlcrn8w/X19br55pt1++23a8aMGf3QVAAATg0RQ3j69OkqLS2VJJWXl8vtdvsPQUvS8uXLdf3112vmzJnRayUAAHEo4uHoKVOmKCcnR/n5+bIsSwUFBSopKVF6erpmzJihtWvXqqKiQmvWrJEkzZs3TwsXLox6wwEAiHW9Oie8dOnSoOGJEyf63+/YsaN/WwQAwCmCu2oAAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhvQqhIuKirRw4ULl5+fr3XffDZq2adMmXXPNNVq4cKEee+yxqDQSAIB4FDGEt2zZooqKChUXF6uwsFCFhYVB0x944AGtXLlSq1ev1saNG7V79+6oNRYAgHgSMYTLysqUl5cnSZowYYLq6upUX18vSdq7d68yMjI0cuRIJSQkaNasWSorK4tuiwEAiBOOSDN4vV7l5OT4h7OysuTxeOR0OuXxeJSVlRU0be/evT2uz+VK70NzB26dplDL4BQvtcRLHRK1DFbxUstA1XHCF2bZth2NdgAAcMqJGMJut1ter9c/XF1dLZfLFXZaVVWV3G53FJoJAED8iRjC06dPV2lpqSSpvLxcbrdbTqdTkjRmzBjV19dr3759amlp0fr16zV9+vTothgAgDhh2b04vvzwww9r27ZtsixLBQUF2rlzp9LT0zVnzhxt3bpVDz/8sCRp7ty5WrRoUdQbDQBAPOhVCAMAgP7HHbMAADCEEAYAwJCIvxMeTIqKirR9+3ZZlqVly5Zp8uTJ/mmbNm3SihUrlJiYqJkzZ2rJkiUGWxpZT7Xk5uYqOztbiYmJktrPyY8YMcJUUyPatWuXFi9erBtuuEHXXntt0LRY6pee6oi1PnnooYf09ttvq6WlRd/85jc1d+5c/7RY6hOp51pipV8aGxt111136eDBgzp27JgWL16s2bNn+6fHUp9EqiVW+iRQU1OT5s2bp8WLF+uqq67yjx+QfrFjxObNm+1bbrnFtm3b3r17t71gwYKg6Zdddpm9f/9+u7W11f7KV75if/DBByaa2SuRapk9e7ZdX19vomkn7OjRo/a1115r33PPPfZzzz0XMj1W+iVSHbHUJ2VlZfY3vvEN27Zt+9ChQ/asWbOCpsdKn9h25FpipV/WrVtnP/XUU7Zt2/a+ffvsuXPnBk2PpT6JVEus9EmgFStW2FdddZX90ksvBY0fiH6JmcPR8XT7zJ5qiTVJSUl6+umnw/4+PJb6pac6Ys2nP/1p/exnP5MknXbaaWpsbFRra6uk2OoTqedaYsnll1+um2++WZJ04MCBoD3DWOuTnmqJRXv27NHu3bt1ySWXBI0fqH6JmcPR/X37TJN6qsWnoKBAH3/8sT71qU/pzjvvlGVZJpoakcPhkMMR/j+jWOqXnurwiZU+SUxMVFpamiRpzZo1mjlzpv/QYCz1idRzLT6x0i+SlJ+fr8rKSj3xxBP+cbHWJz7havGJpT558MEHde+992rt2rVB4weqX2ImhLuy4+iXVV1r+fa3v63Pfe5zysjI0JIlS1RaWqpLL73UUOsgxWafvPrqq1qzZo2eeeYZ003ps+5qibV+eeGFF/Tee+/pe9/7nl5++eVBHU6RdFdLLPXJ2rVrdeGFF+qMM84w1oaYORwdT7fP7KkWSZo/f75OP/10ORwOzZw5U7t27TLRzD6LtX7pSaz1yYYNG/TEE0/o6aefVnp6543oY7FPuqtFip1+2bFjhw4cOCBJ+sQnPqHW1lYdOnRIUuz1SU+1SLHTJ5L02muv6R//+IcWLFigP/zhD3r88ce1adMmSQPXLzETwvF0+8yeajly5IgWLVqk48ePS5K2bt2qc845x1hb+yLW+qU7sdYnR44c0UMPPaQnn3xSw4YNC5oWa33SUy2x1C/btm3z78V7vV41NDQoMzNTUuz1SU+1xFKfSNKjjz6ql156SS+++KK+/OUva/HixfrsZz8raeD6JabumBVPt8/sqZZnn31Wa9euVXJysiZNmqR777130B622rFjhx588EF9/PHHcjgcGjFihHJzczVmzJiY6pdIdcRSnxQXF2vlypUaP368f9xnPvMZnXfeeTHVJ1LkWmKlX5qamnT33XfrwIEDampq0m233aba2tqY/PsVqZZY6ZOuVq5cqdGjR0vSgPZLTIUwAADxJGYORwMAEG8IYQAADCGEAQAwhBAGAMAQQhgAAEMIYQAADCGEAQAw5P8DAPlwCIKKmXgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------\n",
            "25/25 [==============================] - 9s 364ms/step - loss: 0.2000 - acc: 0.9409 - precision: 0.9446 - recall: 0.9409 - auc: 0.9878\n",
            "loss score: 0.19998182356357574\n",
            "accuracy score: 0.9409499168395996\n",
            "precision score: 0.9445876479148865\n",
            "recall score: 0.9409499168395996\n",
            "auc score: 0.9878337383270264\n",
            "\n",
            "Time to run cell: 217 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8n9LOLcdDATt"
      },
      "source": [
        ">While the above model seems to be performing extremely well with an accuracy of 94.6%, we would like to improve the recall rate (94.6%) for our COVID class. Since COVID-19 is able to spread so easily, the less people that are diagnosed as false negatives, the better. 6% may seem like a small number of false negatives, but if we were to diagnose 1,000,000 people who were COVID positive, we would have around 60,000 COVID positive cases that were wrongly identified as being another class.\r\n",
        "\r\n",
        ">Based on prior experience when working with pneumonia chest x-rays in my previous project, it is extremely important for us to feed in chest x-ray images of high quality in order to score extremely well. For this next portion, we will be using CLAHE as a preprocessing technique to enhance the images before they are fed into the model. In doing so, hopefully we will see an increase in recall rate for the COVID class and an overall increase in accuracy. \r\n",
        "\r\n",
        ">- Link to [Pneumonia Project repo](https://github.com/Lewis34cs/cloudy_lungs)\r\n",
        "  - Part 1: [`pneumonia_proj.ipynb`](https://colab.research.google.com/drive/1pMKRsWUJ5pnAGUp_W01nB9fitP57F0Sc)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0E1fNRXEiq9"
      },
      "source": [
        "# removing unpreprocessed CXR train, test, and val folder directory\r\n",
        "try:\r\n",
        "  shutil.rmtree(cxr_folders)\r\n",
        "except:\r\n",
        "  pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xRCHNNzvLUi0"
      },
      "source": [
        "## CLAHE Preprocessing Techniques"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ynVOM8qnBwZy"
      },
      "source": [
        ">Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to equalize pixel intensity in images. It is very similar to Adaptive Histogram Equalization (AHE), except it doesn't over-amplify the contrast of the image. This is controlled by the clipLimit parameter. The way CLAHE works on an image is that it focuses on small portions of the image (tileGridSize parameter) and then combines these portions together through bilinear interpolation to help remove any artificial boundaries, which means that it enhances the local contrast of the total image. This essentially helps with the pixel intensity distribution, allowing us to see more \"depth\" in an image."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "foGMXTKUQqW4"
      },
      "source": [
        ">link for info on cv2.createCLAHE(): https://docs.opencv.org/master/d6/dc7/group__imgproc__hist.html#gad689d2607b7b3889453804f414ab1018"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QqzwkHBBUGYE"
      },
      "source": [
        ">Here we are splitting up classes so we are able to view an example image of each class before CLAHE and then comparing the before-and-after images side by side."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yWdtiheYCn1S"
      },
      "source": [
        "# Defining classes\r\n",
        "covid = ('Covid Class', 'COVID/COVID')\r\n",
        "normal = ('Healthy Class', 'NORMAL/NORMAL')\r\n",
        "pneumo = ('Viral Pneumonia Class', 'Viral Pneumonia/Viral Pneumonia')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnyvpGuvSy8R",
        "outputId": "ee5d8d6c-9217-45d4-d742-e4b8732ab9b3"
      },
      "source": [
        "class_select = input(f'Please select one of the classes to view: '\r\n",
        "                     f'[covid][normal][pneumo] ')\r\n",
        "if class_select == 'covid':\r\n",
        "  selected_class = covid\r\n",
        "elif class_select == 'normal':\r\n",
        "  selected_class = normal\r\n",
        "elif class_select == 'pneumo':\r\n",
        "  selected_class = pneumo\r\n",
        "try:\r\n",
        "  # choosing a random image within the selected class folder\r\n",
        "  i = np.random.choice(range(len(os.listdir(cxr_base_folder+selected_class[1].split('/')[0]))))\r\n",
        "  img = cv2.imread(cxr_base_folder + selected_class[1] + f' ({i}).png', 0)\r\n",
        "  print(f'Successfully loaded in an image from {selected_class[0]}')\r\n",
        "  print(f\"Image size: {img.shape}\")\r\n",
        "except:\r\n",
        "  print('Error: No class with that name exists')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Please select one of the classes to view: [covid][normal][pneumo] covid\n",
            "Successfully loaded in an image from Covid Class\n",
            "Image size: (256, 256)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "j4VVazLFLaf7",
        "outputId": "f4acd8f0-8654-4211-cd3b-db51dcd7c0fd"
      },
      "source": [
        "# Plotting an image to view, along with distribution of pixel intensity\r\n",
        "\r\n",
        "plt.title(selected_class[0])\r\n",
        "plt.imshow(img, cmap='gray')\r\n",
        "hist,bins = np.histogram(img.flatten(),256,[0,256])\r\n",
        "\r\n",
        "cdf = hist.cumsum()\r\n",
        "cdf_normalized = cdf * hist.max()/ cdf.max()\r\n",
        "\r\n",
        "plt.figure(figsize=(6, 6))\r\n",
        "plt.title('Distribution of Pixel Intensity')\r\n",
        "plt.plot(cdf_normalized, color = 'b')\r\n",
        "plt.hist(img.flatten(),256,[0,256], color = 'r')\r\n",
        "plt.xlim([0,256])\r\n",
        "plt.legend(('cdf','histogram'), loc = 'upper left')\r\n",
        "plt.show()\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Jc90d0sCoTU7tq+Bnxd+AT9xOAlixYgXz58/n2WefZdeuXURGRvLZZ5+RkpLCa6+9xq233up2iiLimi7AL4EbgcuBlsf9vAqopHH3dE8mok5Z+lXx9xXr16/n8ccf5+OPP6ayspLMzEzuv/9+5s6dy7hx41iwYIHbKYpIszodp9jfCPTDubJfDzwDfAlsBXb8GLubIZ/ah3QGH+iWVFMc313JwD5Wd8QmC3X1VCjqGx0Nnjc4aGAG6wwmGvRwNS+vjOopIiLV+TkwG+gEzACm4Vzt+wcVfxGRegkF/h/wZ2ADMAjIcjWjhlDxFxGpszbAO8AAYBZwN3DA1YwaSsVfRKROTgXeAy4DRgMvuZtOI6n4i4jUyf/h9OS5BXjD5VwaT0/4iojU6tfA7cBjBELhBxX/BomNjSUn58Sn6B599FH69+9f4+uSk5NJSEhoytRExOsicMYR+xT4i8u5eI/fF39vd5BtjIkTJ/Lhhx/W+PPrrruOHj16NHIvjrCwMK9sR0Rq8yjOkAz34DydGxj8vvi7JSwsjBdffJF169bxwQcf0KpVK2bOnMmwYcMAePLJJ/n666/Jysri6aef5ic/+QlDhw7l6aefJjMzk7POOouePXvy+eefk5WVxTvvvENEhPNY9qWXXkpWVhaZmZk89dRTnr8yUlNTWbBgAR9++CEffvghbdq0Yfny5axZs4bs7GyGDh0KOH+ZbNiwgZkzZ/LNN9/w6quv0r9/fz799FPy8vJISkpy56SJ+J14nB49LwLZLufifT7wlFzdnlIzTnzC17wcdckrNjbWDh06ZD179jTA3njjDbv11ltt5syZNmzYMOvYsaPl5uZ61u/QoYMBnp8fWZ6VlWVXXHGFAfboo4/alClTDLCcnBzr27evAfbkk09aTk6OAZaammpFRUUWGRlpgIWFhVm7du0MsE6dOll+fv4x+V1wwQUWEhJiX375pc2YMcMAGzp0qM2fP7/a49ITvgrF8fGSwb8NzvCBXOoemsylCRUWFpKV5TzYsWbNmmMmP9+9ezcHDx5kxowZXH/99Rw4cGI/4Pbt2xMREcEnnzhD1c2aNYsrrriCDh060K5dO1atWgU402gebdmyZZSXlwPOfMZPPPEEWVlZLF++nOjoaLp06eLJb926dZgZX3/9tac5KicnRxO1i9RJHJAKvABsczeVJqDi30A//PCD5+vKykpatGhxzPe9e/fmrbfe4tprr2XJkiVe2+/+/fs9X996662cdtpp9OrVi8TERMrKymjVqtUJ+VVVVXm+r6qqOiZXEanJvTgXyYE597aKfxNo06YNHTp04P333+d3v/sdPXv2BJxJ3du1awfAnj17KC8v5/LLLwecaSE//vhjdu/ezd69e+nduzcAKSkpNe6nQ4cObN++ncOHD3PVVVfpil7Ea04FRgLzcUbkDDy6BGwC7dq1Y8GCBbRq1YqQkBAeeOABANLT00lLS+O+++7jhhtuIDU1leeff57WrVuzadMmRowYAcCoUaNIS0ujqqrK84FQnddee41FixaRnZ3Nl19+yYYNG5rtGEUC2804PXymuZ1Ik3L95kRNEaxDOrdp08bz9bhx4+y5555rlv3qhq9CcSS+Msj2gTwaFhrS2U8NGTKEhx56iBYtWrBlyxZuv/12t1MSCSI/ARKBO91OpEmp+PugefPmMW/ePLfTEAlS9+DMtvWq24k0Kd3wFRHxaA8MA+YA+2tZ17/5XfEPMdPQBk0gLCwMM3M7DRGXXQ+0ItCv+sEPi3/bzZsZMmSIPgC8KCwsjCFDhrB582a3UxFx2c3AJmC124k0Ob9r87/guefoNXYsw4YNIyQkxO10AoKZsXnzZp577jm3UxFx0WlAfwL1oa7j+V3xb1lezsSJE91OQ0QCzo04JfF1txNpFn7X7CMi0jRuBtb9GIFPxV9EhG7A5QTLVT/UofjPmDGDsrKyameueuCBBzAzOnXq5Fk2depU8vPzycrKIjEx0bN8+PDh5OXlkZeXx/Dhw72UvoiIN/zqx3/TXc2iuZ30EeB+/fpZYmKiZ0z5IxETE2NLliyxzZs3W6dOnQywQYMG2eLFiw2wPn362KpVqwywyMhI27hxo0VGRlpERIRt3LjRIiIi6v2IslH3MfcVCoWi7vGZwRofyMM74ZXx/FeuXMmuXbtOWD5lyhT++Mc/HtM3PDk5mdmzZwOwevVqIiIiiIqKYuDAgZ5x6L///nuWLVvGNddcU9uuRUSaQRTwU5wRPINHg9r8hw4dSklJCdnZx05rFh0dTVFRkef74uJioqOja1wuIuK+oT/+G1zFv95dPU899VQmTJjAgAEDmiIfRo8ezZgxYwDo3Llzk+xDROQ/rgcKgK/dTqRZ1fvK/+yzzyY+Pp6srCwKCwuJiYnhq6++okuXLpSUlNCtWzfPujExMZSUlNS4vDppaWkkJSWRlJTEzp07G3BIIiJ11R64GnjX7URcUeuNgdjY2BNu+B6JwsJCzw3fwYMHH3PDd/Xq1QbODd9NmzZZRESERURE2KZNmzyTkJ8sdMNXoVA0baQYmMFPfSAX70VdbvhS2wpz5861rVu3WkVFhRUVFdnIkSOP+fnRxR+wadOmWUFBgWVnZ1uvXr08y0eMGGH5+fmWn59vt99+e4MO4MffkusnVqFQBEqkG5QahPpALt4LrxR/XzoAQ8VfoVB4K8IN9hi84AO5eDe80tVTRCQw9QfaEazt/Sr+IhKkrgP2Ah+6nYgrVPxFJAiFAsnAYqDC5VzcoeIvIkGoL9CFYHuw62gq/iIShK7HueJ/3+1EXKPiLyJB6Dqctv49bifiGhV/EQkyFwD/RbD28jnCL4u/uZ2AiPix64AqYIHbibjKL4u/iEjD3QCsAsrcTsRVKv4iEkQSgJ4E24xd1VHxF5EgcjNQCcxzOxHXqfiLSBBJAVYQ7E0+oOIvIkHjUqA78LrbifgEFX8RCRI3Az8A77idiE9Q8ReRIBCK0+SzGNjtci6+QcVfRILAFUBX1OTzHyr+IhIEbsEZvvkfbifiM1T8RSTAhQPDcIZz+LfLufgOFX8RCXADgY7AXLcT8Skq/iIS4G4GdgLL3U7Ep6j4i0gAa4MzY9c84LDLufgWFX8RCWBDgdaol8+JVPxFJIDdAhQBn7mdiM9R8ReRAHU6zs3e19EsICdS8ReRAHU70BJ42eU8fJOKv4gEqDuAT4Bv3E7EJ6n4i0gAugpnBM80l/PwXSr+IhKARgPlwFtuJ+KzVPxFJMB0xBnO4VXgoMu5+C4VfxEJMLcBp6Amn5NT8ReRADMGWAXkuJ2IT6u1+M+YMYOysjJycv5zIp966ik2bNhAVlYW77zzDh06dPD8bPz48eTn55Obm8uAAQM8ywcOHEhubi75+fmMGzfOy4chIgLOuP09gOluJ+IX7GTRr18/S0xMtJycHM+y//7v/7awsDADbNKkSTZp0iQDLCEhwdauXWvh4eEWFxdnBQUFFhoaaqGhoVZQUGDx8fHWsmVLW7t2rSUkJJx0v4BlZGQc870dFbW9VqFQBGO8brDLoJUP5OJeHF87q4tar/xXrlzJrl27jlm2bNkyKisrAVi1ahUxMTEAJCcnk56eTkVFBZs3b6agoIDevXvTu3dvCgoKKCws5NChQ6Snp5OcnFzbrkVE6uF04JfAK+hGb+0a3eY/cuRI3n//fQCio6MpKiry/Ky4uJjo6Ogal4uIeM9InIlbnnc7Eb/QojEvnjBhAocPH+a1117zVj6MHj2aMWPGANC5c2evbVdEAlkozo3eFUCey7n4hwYX/9TUVK699lr69+/vWVZSUkK3bt0838fExFBSUgJQ4/LjpaWlkZbmdNHKyMhoaHoiElQGAvHAH91OxK/UemMgNjb2mBu+AwcOtK+//to6d+58zHo9evQ45obvxo0bLTQ01MLCwmzjxo0WFxfnueHbo0ePet+0MHTDV6FQVBcLDLYZtPSBXNyPutzwpbYV5s6da1u3brWKigorKiqykSNHWn5+vn377beWmZlpmZmZNn36dM/6EyZMsIKCAsvNzbVrrrnGs3zQoEH2zTffWEFBgU2YMKFBB2Co+CsUiuOjm8Fhg8d8IBffCK8Uf186AEPFX6FQHB9/Mag0ONMHcvGN8EpXTxER39UCZ+jm94BvXc7Fv6j4i4gfSwbOQN0760/FX0T82F3AZmCJy3n4HxV/EfFT5wL9gReBKpdz8T8q/iLip+4CKoCX3E7EL6n4i4gfaoMzQfubwA53U/FTKv4i4oduAToAf3c7Eb+l4i8ifugeYC3wuduJ+K1GDewmItL8fgr0xBnITRpKV/4i4mfuBHYDc91OxK+p+IuIH4kEbgReBfa7nIt/U/EXET8yHGiF07dfGkPFX0T8yG+AVUC224n4Pd3wFRE/cTmQAIxwO5GAoCt/EfETvwG+B95wO5GAoOIvIn4gErgB50bvv13OJTCo+IuIH7gZ50ZvmtuJBAwVfxHxAyOBr9CNXu9R8RcRH9cT6AW87HYiAUXFX0R83AjgB/REr3ep+IuIDwsHbgXmA+Uu5xJYVPxFxIf9AugMzHQ7kYCj4i8iPmwkUAQsdzuRgKPiLyI+qiswEHgFzdHrfSr+IuKjhgNhOMVfvE3FX0R8UAhOk88/gU3uphKgVPxFxAf1B7oDL7mdSMBS8RcRH3QPsB140+1EApaKv4j4mDNxuni+CFS4nEvgUvEXER9z54//vuBqFoFOxV9EfMgpwGjgXaDY5VwCW63Ff8aMGZSVlZGTk+NZFhkZydKlS8nLy2Pp0qVERER4fjZ16lTy8/PJysoiMTHRs3z48OHk5eWRl5fH8OHDvXwYIhIYfoXzRO/f3U4kKNjJol+/fpaYmGg5OTmeZZMnT7Zx48YZYOPGjbNJkyYZYIMGDbLFixcbYH369LFVq1YZYJGRkbZx40aLjIy0iIgI27hxo0VERJx0v4BlZGQc870dFbW9VqFQ+FuEGeQaZPtALv4dx9fO6qLWK/+VK1eya9euY5YlJycza9YsAGbNmsV1113nWT579mwAVq9eTUREBFFRUQwcOJBly5ZRXl7O999/z7Jly7jmmmtq27WIBJURwLnAn9xOJCg0qM2/S5culJaWAlBaWkqXLl0AiI6OpqioyLNecXEx0dHRNS4XEXG0Ah4BPgMWuZtKkGjhjY2YmTc2A8Do0aMZM2YMAJ07d/badkXEl/0WiAZS3E4kaDToyr+srIyoqCgAoqKi2L59OwAlJSV069bNs15MTAwlJSU1Lq9OWloaSUlJJCUlsXPnzoakJyJ+JQJ4CHgP+NTlXIJHg4r/woULSU1NBSA1NZUFCxZ4lh/pydOnTx92795NaWkpH3zwAQMGDCAiIoKIiAgGDBjABx984KVDEBH/9gegA84HgDSnk94Rnjt3rm3dutUqKiqsqKjIRo4caR07drTly5dbXl6eLVu2zCIjIz3rT5s2zQoKCiw7O9t69erlWT5ixAjLz8+3/Px8u/322xt0x9pQbx+FIrDiDIP9BnN8IJfAibr09sHtJOtzAIaKv0IRWDHT4AeDeB/IJXDCK109RUSaxpXA7cDTQKG7qQQhFX8RcUE48DywEfiry7kEJ6909RQRqZ9xwHk40zQedDmX4KQrfxFpZt1xnuKdCyx1OZfgpeIvIs1sOvBv4AG3EwlqavYRkWY0EmeKxt8AZS7nEtx05S8izeQ84H+AFUCay7mIir+ININWwDxgP3ArTldzcZOafUSkGTwHXIjTu6fU5VwEdOUvIk3ubpw2/kmod4/vUPEXkSY0BKedfyGapMW3qPiLSBO5BHgDyARuBqrcTUeO4bfFX7eLRHzZmcA/gB3AtcABd9M5iWCtJbrhKyJe1hFYDJyK06df/fl9kYq/iHhRW+B94GzgGmCDu+lIjVT8RcRLWuHc2E0Efgl87G46clIq/iLiBeHAWzhj9N+K094vvkzFX0QaqSXwJk63ztFAurvpSJ34bW8fEfEFLXCK/VDgLuAld9OROtOVv4g0UBjOmPy/BH6LMzOX/wjWLp5H6MpfRBogDJgD3Aj8DpjmbjpSbyr+IlJPLYDZOE/t/gFn0DbxN2r2EZF6aI3Tq2cQzjy8f3M3HWkwFX8RqaNOwHqpi+8AABzpSURBVHvApcAdwAx30/Gio9v/Q1zLonmp+ItIHZwJfADE4tzgXehuOtJoKv4iUovzcQp/G2AA8Km76YhX6IaviJzE9TjFPgTohwp/4FDxF5FqhOLMvPUOkA/8BFjnakbiXWr2EZHjtAFeA5JxHty6DzjkakbifSr+InKUbjg3cy/EeWpXD28FKhV/EflRH+BdnElYhuDc5JVA1ag2/7Fjx7Ju3TpycnKYO3cup5xyCnFxcaxatYr8/HzS09Np2bIlAOHh4aSnp5Ofn8+qVauIjY31ygGIiDekAP8E9uO076vwB7oGF/+uXbty3333cemll3LhhRcSFhZGSkoKkydPZsqUKXTv3p3y8nJGjRoFwKhRoygvL6d79+5MmTKFyZMne+0gRKShQoBHgdeB1ThX/5p9K1hYQ6Jr16727bffWmRkpIWFhdmiRYtswIABtmPHDgsLCzPA+vbta0uWLDHAlixZYn379jXAwsLCbMeOHbXuIyMj45jv7bhoaO4KhQKDUw3eMDCDGQYtfSCnpg876t+awu0cGxvH187qosFX/lu3buVvf/sb3377Ldu2bWP37t2sWbOG77//nsrKSgCKi4uJjo4GIDo6mqKiIgAqKyvZvXs3nTp1aujuRaRRzsCZZvEG4EFgFOrRE1waXPwjIiJITk4mPj6erl270qZNG6655ppGJzR69GgyMjLIyMigc+fOjd6eiBwvEcgAzsPpzvmsu+mIKxpc/H/+859TWFjIzp07OXz4MO+88w6XXXYZERERhIWFARATE0NJSQkAJSUldOvWDYCwsDA6dOjAd999d8J209LSSEpKIikpiZ07dzY0PRGp1rXASuAwcBnBNNfukfYOjvo3mDW4+H/77bf07duXU089FYD+/fuzfv16PvroI2644QYAUlNTWbBgAQALFy4kNTUVgBtuuIEVK1Y0NncRqZe7cbpyrse5sZvjbjriugbfVHjkkUdsw4YNlpOTY7Nnz7bw8HCLj4+31atXW35+vs2bN8/Cw8MNsFNOOcXmzZtn+fn5tnr1aouPj6/3TYtAuymjUDRPhBg8bWAG7xq09oGcmj+sHuF2ro2Nutzwxe0k63MAgfYLUiiaPk4xSDcwg/81CPWBnNwJq2e4nW9joi7FX0/4igSsCJxmniuB3wPPuJuO+BQVf5GAFAssBs7GeXr3DXfTcZm5nYAPUvEXCThXAW8CYTiTr3ziajbimzSev0hA+S2wDCgDklDhl5qo+IsEhHCcCdX/B1gE9AU2upqRNx25Syneo+Iv4vda44zBPxJ4BBgG7HMzIfEDavMX8WvtgfdwhmEeAbziajbiP1T8RfxWZ5xx9y8AfgW87W46TUTNPU1DxV/EL3UFluN06RyKJl+R+lLxF/E7Z+EU/k7AQOBTd9MRv6TiL+JXLsVp4w8FrgbWuJuOH1CzUfX8urePun9JcBmCM8/uPpzhmFX4peH8uviLBI8xwAKc+XV/AuS5m474PRV/EZ/3GPACsARn6IbtrmYjgUFt/iI+qyWQBqQCL+JMxlLpakYSOHTlL+KT2uHc2E0FHgZ+gwq/Q/f5vCMgrvwNCHE7CRGv6YozHHMP4HZglqvZ+IMjHwiqA3UXEMVfJHD0AN4HInF69yxzNx0JWGr2EfEZVwKf4VyT9UOFX5qSir+IT0jBGaJhK05Xzix30/FRdtS/dtyy47+Wk1PxF3Hd74HXgVU4D2996246AUIPgZ6cir+Ia0JxJl95GmeO3YHA965mFChU9Gun4i/iijbAWzjTLv4NuBn4wdWMAoW3Cn+gf4Cot49IszsTZ+atC4D7gP91Nx0fFugF2E0q/iLN6qfAfJw5dwcDS91Nx0f5StEP5OcH1Owj0mxGAB/htOv3QYW/8XzlQ8IfqfiLNLkw4BngZZwhmfugUTnFbWr2EWlSkUA6MACYCjyIxugRX6DiL9JkzscZgz8GGAnMdDcdkaOo2UekSdwErAZOxRm2QYVffIuKv4hXhfGfh7bW4sy5u9rVjESq06ji36FDB9588002bNjA+vXr6du3L5GRkSxdupS8vDyWLl1KRESEZ/2pU6eSn59PVlYWiYmJjU5exLechjMY2+9x+u7/DNjmakYiNWlU8Z86dSpLliwhISGBnj17smHDBsaPH8+HH37IOeecw4cffsj48eMBGDRoEN27d6d79+6MGTOG6dOne+UARHxDEs6E6n2B4TgPbx1yNSOR2lhDon379rZp06YTlufm5lpUVJQBFhUVZbm5uQbY888/bykpKdWuV1NkZGQc872dJBp6HApF4+MOg4MGmwwu9oF8/D/Mx8Lt81HfOL52VhcNvvKPj49nx44dzJw5k6+++oq0tDRat25Nly5dKC0tBaC0tJQuXboAEB0dTVFRkef1xcXFREdHN3T3Ij4gHGdu3TSc/vuX4rTzi/i+Bhf/Fi1acMkllzB9+nQuueQS9u/f72niOZqZ1Wu7o0ePJiMjg4yMDDp37tzQ9ESaWAywEhgNPI4zVMMuVzMSqY8GF//i4mKKi4v54osvAHjrrbe45JJLKCsrIyoqCoCoqCi2b98OQElJCd26dfO8PiYmhpKSkhO2m5aWRlJSEklJSezcubOh6Yk0oZ/htO+fB1yHM8F6lasZidRXg4t/WVkZRUVFnHPOOQD079+f9evXs3DhQlJTUwFITU1lwYIFACxcuJDhw4cD0KdPH3bv3u1pHhLxH3/E6dGzE+iN8xCXiH9q8E2Fnj17WkZGhmVlZdn8+fMtIiLCOnbsaMuXL7e8vDxbtmyZRUZGetafNm2aFRQUWHZ2tvXq1aveNy0C6YaMwt+incHbBmaQbtDGB3IK3DAfC7fPR32jLjd8cTvJ+hxAIP1yFP4U5xvkGhwyGOsD+QR+mI+F2+ejvlGX4q+xfURO6mac3jx7gKtxbvKK+D8N7yBSrZY48+vOBb4CLkGFXwKJir/ICaKBj3Hm130G54pfnRMksKjZR+QYV+OMv98KuBFnknWRwKMrfxGPB3GmVtyBM1aPCr84zO0EmoCKvwinAK8AfwPewem//42bCYk0ORV/CXJROJOqpwL/D2cSlv2uZhTsAvEq2xepzV+C2CU4T+hGAsNwrvpFgoOu/CVI3YTTdbMSuAwVfgk2Kv4SZEKAv+BMs/gVzo3dLFczEnGD3zT7qB1QGq8dzo3dXwIzgLuBCjcTEnGN3xR/kca5BOdqPw64H+fpXZHgpWYfCQL3AJ/jdOm8ChV+ERV/CWgtgReAaTgPb10MfOZqRiK+QsVfAlRH4ANgDM40i0PRNIu+7chYw9I81OYvAeg8YBHQDbgNeNXddER8kIq/BJgBwDzgIE77/ipXsxHxVWr2kQByL7AY2IwzPo8Kv0hNVPwlALQApgP/C/wD54ndb13NSMTXqfiLn4sElgB3ApOA69HAbCK1U5u/+LFzcG7sxgLDgTnupiMN4i89fAxncJBAoeIvfqo/zmQrFTizb/3L3XRE/IyafcQP3YnT1FOEc2NXhV+kvlT8xY+cgvO07nSc4n8ZsMXVjET8lZp9xE+cjdPMczHOdIvjgCpXMxLxZyr+4gcGAOk4xX4ITl9+EWkMNfuIDwsB/ohT7L8FLkWFX8Q7dOUvPioSZ+KVoTjDNYwADriZkEhA0ZW/+KAknCkWrwF+C/wKFX4R71LxFx9zJ/Dpj19fjtO7RwKRvzzcFahU/MVHhABP4nTjXAokAhmuZiRNTx8A7ml08Q8NDeWrr75i0aJFAMTFxbFq1Sry8/NJT0+nZcuWAISHh5Oenk5+fj6rVq0iNja2sbuWgHEqzvy643GKfzLwvasZiQS6Rhf/+++/nw0bNni+nzx5MlOmTKF79+6Ul5czatQoAEaNGkV5eTndu3dnypQpTJ48ubG7loAQBXwMDAMeBO5G/fdFmoc1NKKjo2358uX2s5/9zBYtWmSA7dixw8LCwgywvn372pIlSwywJUuWWN++fQ2wsLAw27FjR63bz8jI8HxttURjjkPhVlxs8K3BXoNf+EA+iuYM88Nw+5zVNY6unTVFo678n3vuOf74xz9SVeVcqXXq1Invv/+eyspKAIqLi4mOjgYgOjqaoqIiACorK9m9ezedOnVqzO7FryXj3Ng1nGEaFrmbjkiQaXDxHzJkCNu3b+err77yZj6MHj2ajIwMMjIy6Ny5s1e3Lb5iJPAOsA5nYLZsd9MRCUINfsjrsssuY+jQoQwePJhWrVrRvn17pk6dSkREBGFhYVRWVhITE0NJSQkAJSUldOvWjZKSEsLCwujQoQPffffdCdtNS0sjLS0NgIwM9fYIPL8B/g/4APglzly7EkzM7QQEaMSV/4QJE+jWrRvx8fGkpKSwYsUKfv3rX/PRRx9xww03AJCamsqCBQsAWLhwIampqQDccMMNrFixwgvpi/8IB14AnscZkVOFX8Rtjb65cOWVV3pu+MbHx9vq1astPz/f5s2bZ+Hh4QbYKaecYvPmzbP8/HxbvXq1xcfH1+umRaDciAnOiDFY/eOv6q8GoT6Qk8KtMD8Ot89dXaMuN3xxO8m6HkCg/FKCL64yKDPYbZDsA/ko3A7z43D73NU1mry3j8jJPQgsB3bijNezwN10RBrpSOUMBCr+0gTa44y//zdgPtAHyHM1IxE5loq/eFlfnK6bw3Bm27oR2OdqRiJyIhV/8aIxwD+BwzgPbj3lajYiUjNN5iJe0BGYAwzG6b9/M1DuakbimwKlvTwQ6MpfGikBWAX0B+4FBqHCL+L7dOUvjXArzoNb+4CrgX+5m46I1Jmu/KUBWuEU/VeBL3EmXlHhF/EnKv5ST2fjFPoxwBM4zT3bXM1IROpPzT5SD8OAl3F68wwBFrubjog0mK78pQ5OAZ4D3gLW4zTzqPBL/ainj29R8ZdanIfTm+d+nA+AK4BvXc1IRBpPzT5yEqNxCv5+1MwjDaGrfd+lK3+pRnvgTeBFnKkWL0KFXySwqPjLcS4AMoDrgD8A1wClrmYkIt6n4i9HuQNYDbQFfoYzKqf+cJeG0TvHt6n4C9AVp1knDfgcuASnuUdEApWKf9C7BVgHXIkzNs9/A2WuZiT+4+jJTXSl719U/IPWaTj99l/D6bvfE/g7zfVfOJBmRJJjBcPvNhCOT8U/KP0S52r/WpybulcABa5mJCLNS/38g0p3YCrOsMtrcG7qrnc1IwkMgXAlHGx05R8U2gBP4lzt/xQYizPdogp/IPGF5ha39y91pyv/gNYeuAun2EcBM4HxwHY3kxIvOlJsQwJ0f9J0VPwDUhecgn8X0AFnasXrcPrwy9F8tZh5Oy/z4raaYnvS/AKm+OvNCM7Tub/DmWGrBc4QDZOBtW4mJXXgqx9CErgCpvgHtwHAgz/+ux/nYa3ngI1uJtVgjf0gb8jrm/PiobZCb9V8ffy6jWlbb+ixqj0/sPhF8debrjptgBScK/3zga3AQzjTK2oCdX9Sl/d3fT4wGpOH/vIIHn5R/OWIlsBAnKdyk4HWQCYwHHgDqHAvtTqqqcDUt9nDnwqVN5t06lrkqzs/Nb22pnX95fxKw6j4+7wQ4HKcdvwbgY7ATuAVYC7wmWuZHc8bzQnHF6iTbfP4olrda4/8/PjtNEfTkK8U0OqKvv6aFhV/n3URzhX+zcCZOG357+IMx7AMZx5dOULFrHEaMj6Pzrl/U/H3GWFAEs5N25tw2vEP4XTTHAcsBA64ll19nOyKu6Z167NNN9Ulj5Mdk78VTF85777IV/6ya6gGP+EbExPDihUr+Prrr1m3bh333XcfAJGRkSxdupS8vDyWLl1KRESE5zVTp04lPz+frKwsEhMTG5+934sERgJv4zTlfA5MxLlhexdwBvALIJ3mLvz1LcrNpa5NGG4U4Pput6meyPXWNv3tg0rqzxoSUVFRlpiYaIC1bdvWvvnmG0tISLDJkyfbuHHjDLBx48bZpEmTDLBBgwbZ4sWLDbA+ffrYqlWrat1HRkaGAWZ1jIYeS/NGnMF9Bh8aHPox9UKDFw1uMOjoAznW7Xwef96tmuXH//z4dWr7XVa3fn3eE/XJo6Zjqs8+T7ZeXY+7PsdW13PojW15K69ACrf/n9YUR2pnLeGdnb377rv285//3HJzcy0qKsrA+YDIzc01wJ5//nlLSUnxrH/0erUdgH//Ik41uMrgMYOso9LNMXjcINEHcjwxqjun1X1v1fzcqvn6+N9TXX+X1a1fn/dETdusb1513WdDc/P1CNTj8sZ58cWoS/H3Spt/bGwsiYmJrF69mi5dulBa6sz5WlpaSpcuXQCIjo6mqKjI85ri4mKio6M96waOtsBlOMMkX4nTjh+Oc4N2JU6//IXAJrcSbBTDuw8cnWw/vqSu+fha3t4SqMcVzBpd/Nu0acPbb7/N2LFj2bt37wk/N6vf22b06NGMGTMGgM6dOzc2vWbQEafYX4lT8C/BuXl7CGci9GeBT3C6ZO5xKcfGsxq+buy23Hh9c29XAteR94w/3vhtVPFv0aIFb7/9Nq+99hrz588HoKysjKioKEpLS4mKimL7dmcEyZKSErp16+Z5bUxMDCUlJSdsMy0tjbS0NAAyMjIak14T6Y4zLPJPcPrfn//j8oPAKuBx4OMfv/aP3jnQNG9iXyumvpaPiJsaNZ7/jBkz2LBhA1OmTPEsW7hwIampqQCkpqayYMECz/Lhw4cD0KdPH3bv3u0HTT5tgKuACcAiYAeQh/OA1a+ALThDKlwBROBMjjIRWIE/FP7qiqFx4lW+20XT7f2L1MZf36MNuqFw2WWXmZlZVlaWZWZmWmZmpg0aNMg6duxoy5cvt7y8PFu2bJlFRkZ6XjNt2jQrKCiw7Oxs69WrV51vWjTfzZcuBrcaTDP4yuDwUZv/2uAlg5EGCQYhrt7Q8UbYcV8rFIqGh9v/n6urnSeLI8/h+KSMjAySkpLqnGD9myxa4TTh9P8xknD+GNqL02zz+Y+xmkAcLM2oeWgEEakfX2r3P1I7TybInvA9BejJf4r9ZTgfAIeAL3CabP4BZANVLuXYvFT0RYJTgBf/CJz2+Kt+jItweuIAZAF/Bz7E6YK5r/nTc5GKvkhwC7Di34Fji/3FOM04/wb+BTyBU/Q/wbl5G3xU9EUEfLz4X0rditU6zudh/goMxSn2B3GK/aPARzht9r4/1n1TU+EXkSN8uvjXxXsM5kbeJJwKnPlq38dpv//B3cRERHyYXxf/As7mOt6lJ1ksZjBdgrQpR0Skvhr1kJfbXuIOjBAWMpTTVfhFROrMb4v/IVrwCrczhPfoyjbgP08viM6DiJyc3xb/9xhCGVHcwUtup+K6uhR6fRiIyNH8tvjPYBRnsJVBvO92Kj5LfwmJNB9/+7/ml8W/hK4sZjAjmEkLKt1Ox+f525tSRJqeXxb/V7idKsIYyctupyIi4pf8sqvna9zKlfyTs/10NqzGOnpAtqOXiYhvqO7/qK/xuyv/jZzFBnrwS95xOxUREb/ld8V/Eb8A4BcscjkTERH/5ZfF/3zWEc9mt1NxlZp5RHybr/8f9aviv5e2fMIVAXfVX92bpLqpd2paR0R8gz/9n/SrG75r6MVhWnIFnzTZPo784pr6Zk1D9uMvbyoR8X1+deWfgTMtWRIZta7ry4XSavhaRKS5+F3xj6OQznxX59ecrMnEDbXtVx8GItIc/K741+Wqvya1tZs3pDDX5TX1Lej6ABCRpuY3xX8nndhMfKOK/8mcrODWp4A3tHD7040iEfF/flP8v+RSAC7ly1rXbcpCXduHRE3fq7iLiC/xm94+a7kYgEv4qs6vacyN1foMk3yyoi8iwevoWuBrwz34TfHP5iLOZAsd2ON2KiruIuL3/Kb453AhF5Hd6O2ocIuI+EmbfwUtyeU8LiTH7VRERAKCXxT/XM7jMC29cuUvIiJ+UvyzuQhAV/4iIl7iF8U/hwsJ5wfOIc/tVEREGsTX7jf6fPGvJJT3GMJFZNOSw26nIyISEJq9+A8cOJDc3Fzy8/MZN25creu/zs18zQU8yDPNkJ2ISNM52RDtza1Zi39oaCh///vfGTRoED169ODmm28mISGhxvUrCOdPPM7FZHIT85oxUxGRwNasxb93794UFBRQWFjIoUOHSE9PJzk5ucb1N3Ae3xPBdO4i1Cc+K0VEvMfNqtasxT86OpqioiLP98XFxURHR9e4fhiVrKQffVndHOmJiDSbo4eHceNDwOee8B09ejRjxowB4L8uCGFdxst1GMotsHXu3JmdO3e6nYardA4cOg+Bew7qM15xbecgNja2TtupbrrYJom+ffvakiVLPN+PHz/exo8fX+P6GRkZzZabL4fOg86BzoPOgbfPQbM2+2RkZNC9e3fi4uJo2bIlKSkpLFy4sDlTEBERmrnZp7KyknvvvZcPPviAsLAwXn75ZdavX9+cKYiICBAGPNKcOywoKGDatGn8z//8DytXrqx1/a++qvv4/YFM50Hn4AidB50DaPw5CMFp/xERkSDi88M7iIiI9/ls8a/vMBCBorCwkOzsbDIzM8nIcDp/RUZGsnTpUvLy8li6dCkREREuZ+l9M2bMoKysjJyc/4zcerLjnjp1Kvn5+WRlZZGYmOhGyl5X3TmYOHEixcXFZGZmkpmZyaBBgzw/Gz9+PPn5+eTm5jJgwAA3Uva6mJgYVqxYwddff826deu47777gOB7L9R0Hrz9fnC929LxERoaagUFBRYfH28tW7a0tWvXWkJCgut5NUcUFhZap06djlk2efJkGzdunAE2btw4mzRpkut5ejv69etniYmJlpOTU+txDxo0yBYvXmyA9enTx1atWuV6/k11DiZOnGgPPvjgCesmJCTY2rVrLTw83OLi4qygoMBCQ0NdP4bGRlRUlCUmJhpgbdu2tW+++cYSEhKC7r1Q03nw5vvBJ6/86zsMRKBLTk5m1qxZAMyaNYvrrrvO5Yy8b+XKlezateuYZTUdd3JyMrNnzwZg9erVREREEBUV1bwJN4HqzkFNkpOTSU9Pp6Kigs2bN1NQUEDv3r2bOMOmV1paSmZmJgD79u1jw4YNREdHB917oabzUJOGvB98svjXdxiIQGJmLF26lC+//JLRo0cD0KVLF0pLSwHnTdGlSxc3U2w2NR13sL0/7r33XrKyspgxY4anuSMYzkFsbCyJiYmsXr06qN8LR58H8N77wSeLfzC7/PLL6dWrF4MGDeKee+6hX79+J6xjZi5k5r5gPO7p06dz9tlnc/HFF7Nt2zaeeSY4hjZv06YNb7/9NmPHjmXv3r0n/DxY3gvHnwdvvh98sviXlJTQrVs3z/cxMTGUlJS4mFHz2bp1KwA7duxg/vz59O7dm7KyMs+fslFRUWzfvt3NFJtNTccdTO+P7du3U1VVhZmRlpbm+VM+kM9BixYtePvtt3nttdeYP38+EJzvherOgzffDz5Z/IN1GIjWrVvTtm1bz9cDBgxg3bp1LFy4kNTUVABSU1NZsGCBm2k2m5qOe+HChQwfPhyAPn36sHv3bk+TQKA5uv36+uuvZ926dYBzDlJSUggPDycuLo7u3bvzxRdfuJWmV82YMYMNGzYwZcoUz7JgfC9Udx68/X5w/c52dTFo0CD75ptvrKCgwCZMmOB6Ps0R8fHxtnbtWlu7dq2tW7fOc9wdO3a05cuXW15eni1btswiIyNdz9XbMXfuXNu6datVVFRYUVGRjRw58qTHPW3aNCsoKLDs7Gzr1auX6/k31TmYPXu2ZWdnW1ZWli1YsMCioqI860+YMMEKCgosNzfXrrnmGtfz90ZcdtllZmaWlZVlmZmZlpmZaYMGDQq690JN58Gb7wc94SsiEoR8stlHRESaloq/iEgQUvEXEQlCKv4iIkFIxV9EJAip+IuIBCEVfxGRIKTiLyIShP4/azD2lTl0qbYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-Bd5rlmRkpn"
      },
      "source": [
        ">As we can see here, the distribution of pixel intensity in our unpreprocessed image ranges from 0 to around 220. The way CLAHE works on an image is that it focuses on small portions of the image (tileGridSize parameter) and then combines these portions together through **bilinear interpolation** = a resampling method that uses the distance-­weighted average of the X nearest pixel values to estimate a new pixel value. Bilinear interpolation helps remove any artificial boundaries, which enhances the local contrast of the total image."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfpBLZE-rMO-"
      },
      "source": [
        "### Creating CLAHE preprocessed images and placing them into new directory"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BRYFjyQnVE2M"
      },
      "source": [
        ">Creating a new folder in the 'cxr_clahe_folder' variable to store our CLAHE preprocessed images. If you would like to store the folders in another filepath, please specify where you would like to store your train, test, and validation folders in the 'cxr_clahe_folder' variable. Note that this folder is temporary and will be deleted later on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saZimyENxUbN",
        "outputId": "94de82d3-1602-43da-a68b-e892f5ad8012"
      },
      "source": [
        "# Please define a filepath where you would like the CLAHE preprocessed images to\r\n",
        "# be stored in the 'new_folder' variable. This folder is temporary.\r\n",
        "try:\r\n",
        "  if os.path.exists(cxr_clahe_folder):\r\n",
        "    print('CLAHE folder already exists')\r\n",
        "except:\r\n",
        "  cxr_clahe_folder = ensure_filepath('/content/clahe_radio')\r\n",
        "  clahe_preprocessing(cxr_base_folder, cxr_clahe_folder)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Viral Pneumonia: 100%|██████████| 1345/1345 [00:41<00:00, 32.34it/s]\n",
            "COVID: 100%|██████████| 1200/1200 [00:06<00:00, 197.55it/s]\n",
            "NORMAL: 100%|██████████| 1341/1341 [00:38<00:00, 34.62it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time to run cell: 86 seconds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ovLmSZVJxEX",
        "outputId": "dc4ad8b7-bb00-4e19-d537-be57a8d0d013"
      },
      "source": [
        "num_items(cxr_clahe_folder)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/clahe_radio/\n",
            "Number of items in COVID: 1200\n",
            "Number of items in NORMAL: 1341\n",
            "Number of items in Viral Pneumonia: 1345\n",
            "Total number of items: 3886\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3886"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8iyCKFbsCN3s"
      },
      "source": [
        "# Setting original and CLAHE images to compare\r\n",
        "orig_img = cv2.imread(cxr_base_folder + selected_class[1] + f' ({i}).png')\r\n",
        "clahe_img = cv2.imread(cxr_clahe_folder + selected_class[1] + f' ({i}).png')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "sUSVrgdSNyG8",
        "outputId": "3604ae82-98c1-4181-86d7-3a7fd193ff89"
      },
      "source": [
        "hist,bins = np.histogram(clahe_img.flatten(),256,[0,256])\r\n",
        "\r\n",
        "cdf = hist.cumsum()\r\n",
        "cdf_normalized = cdf * hist.max()/ cdf.max()\r\n",
        "\r\n",
        "plt.figure(figsize=(6, 6))\r\n",
        "plt.title('Distribution of Pixel Intensity Using CLAHE')\r\n",
        "plt.plot(cdf_normalized, color = 'b')\r\n",
        "plt.hist(clahe_img.flatten(),256,[0,256], color = 'r')\r\n",
        "plt.xlim([0,256])\r\n",
        "plt.legend(('cdf','histogram'), loc = 'upper left')\r\n",
        "plt.show()\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8sM2HDu0Wcy1"
      },
      "source": [
        ">Note that we can see that the intensities of pixels, when compared to the original distribution, have become more spread out. This will give our image a 'deeper' look to it, and give our model more information for each image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "kt7c0rwnPNHT",
        "outputId": "152adb81-cc3d-4ecd-bc27-90206c47f105"
      },
      "source": [
        "# Comparing original image (left) to CLAHE applied image (right)\r\n",
        "res = np.hstack((orig_img,clahe_img))\r\n",
        "fig = plt.figure(figsize=(10, 5))\r\n",
        "ax = fig.add_subplot(111)\r\n",
        "plt.imshow(res, cmap='gray')\r\n",
        "plt.title('Original image                           Modified CLAHE image')\r\n",
        "ax.axes.get_xaxis().set_visible(False)\r\n",
        "ax.axes.get_yaxis().set_visible(False)\r\n",
        "ax.set_frame_on(False)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REd_naWSR6mw"
      },
      "source": [
        ">We can definitely see more of the infiltrate in the lung areas of the chest x-ray. These infiltrate areas of the lung can determine whether or not a person has Pneumonia. We can definitely see more of the infiltrate in the lung areas of the chest x-ray. These infiltrate areas of the lung can determine whether or not a person has Pneumonia. According to Hosseiny et al., when there is radiographic appearances of multifocal ground glass opacity, linear opacities, and consolidation, these are usually seen in cases of coronavirus type infections, including COVID-19, SARS, and MERS."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSikbgbvDgdx"
      },
      "source": [
        "# Removing unpreprocessed images from our directory\r\n",
        "try:\r\n",
        "  shutil.rmtree(cxr_base_folder)\r\n",
        "except:\r\n",
        "  pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FySQkEHxW1Zu"
      },
      "source": [
        ">Splitting our preprocess image folder into train, test, and validation subfolders into a new directory. If you would like to store the folders in another filepath, please specify where you would like to store your train, test, and validation folders in the 'clahe_ttv_folder' variable. Note that this folder is temporary and will be deleted later on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9i2Lgc9pXka",
        "outputId": "9995ae8b-b60c-472a-a86e-13e3edc743b2"
      },
      "source": [
        "clahe_ttv_folder = ensure_filepath('/content/clahe_ttv')\r\n",
        "\r\n",
        "splitfolders.ratio(input=cxr_clahe_folder, \r\n",
        "                    output=clahe_ttv_folder, \r\n",
        "                    seed=42, ratio=(0.7, 0.1, 0.2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Copying files: 3886 files [00:05, 721.28 files/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqjlgrLdL5Vd"
      },
      "source": [
        "# Removing base clahe images folder from our directory\r\n",
        "try:\r\n",
        "  if os.path.exists(clahe_ttv_folder):\r\n",
        "    shutil.rmtree(cxr_clahe_folder)\r\n",
        "except:\r\n",
        "  print('Please run the cell above first')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRq-M28cp4wg",
        "outputId": "401066d1-5078-495c-ad24-5301e7561a1e"
      },
      "source": [
        "folder_check(clahe_ttv_folder)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/clahe_ttv/train/\n",
            "Number of items in Viral Pneumonia: 941\n",
            "Number of items in COVID: 840\n",
            "Number of items in NORMAL: 938\n",
            "Total number of items: 2719\n",
            "\n",
            "/content/clahe_ttv/test/\n",
            "Number of items in Viral Pneumonia: 270\n",
            "Number of items in COVID: 240\n",
            "Number of items in NORMAL: 269\n",
            "Total number of items: 779\n",
            "\n",
            "/content/clahe_ttv/val/\n",
            "Number of items in Viral Pneumonia: 134\n",
            "Number of items in COVID: 120\n",
            "Number of items in NORMAL: 134\n",
            "Total number of items: 388\n",
            "\n",
            "\n",
            "Total image count: 3886\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4INXA2zn040"
      },
      "source": [
        "## Modeling with CLAHE preprocessed images"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jPzeqnDGrWAE"
      },
      "source": [
        ">Defining new train, test, and validation folders for our CLAHE preprocessed chest x-ray images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbTg-e-mqKfY"
      },
      "source": [
        "train_folder, test_folder, val_folder = create_ttv(clahe_ttv_folder)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MFF9AJbzqmh-"
      },
      "source": [
        "# Still the same size as our previous model\r\n",
        "IMG_SIZE = (128, 128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6NRm4PgsrDEy",
        "outputId": "a99408dc-808c-41e7-8f53-6f278bca9ea3"
      },
      "source": [
        "train_clahe_datagen = ImageDataGenerator(rescale=1./255)\r\n",
        "\r\n",
        "test_val_clahe_datagen = ImageDataGenerator(rescale=1./255)\r\n",
        "\r\n",
        "cl_train_generator = train_clahe_datagen.flow_from_directory(train_folder, \r\n",
        "                                                   target_size=IMG_SIZE,\r\n",
        "                                                   batch_size=32,\r\n",
        "                                                   class_mode='categorical')\r\n",
        "\r\n",
        "cl_test_generator = test_val_clahe_datagen.flow_from_directory(test_folder, \r\n",
        "                                                     target_size=IMG_SIZE,\r\n",
        "                                                     batch_size=32,\r\n",
        "                                                     class_mode='categorical', \r\n",
        "                                                     shuffle=False)\r\n",
        "\r\n",
        "cl_val_generator = test_val_clahe_datagen.flow_from_directory(val_folder, \r\n",
        "                                                    target_size=IMG_SIZE,\r\n",
        "                                                    batch_size=32,\r\n",
        "                                                    class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2719 images belonging to 3 classes.\n",
            "Found 779 images belonging to 3 classes.\n",
            "Found 388 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cboKKu8mO-X",
        "outputId": "7c1195e9-93d6-47ba-c0d7-a8c3225d9a8d"
      },
      "source": [
        "INPUT_SHAPE = cl_train_generator.image_shape\r\n",
        "print(INPUT_SHAPE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(128, 128, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXLfDEj5ml3w",
        "outputId": "1e345ebb-7f45-4344-a5d6-ecdea05dee5e"
      },
      "source": [
        "class_weights_dict = make_class_weights(cl_train_generator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{0: 1.078968253968254, 1: 0.9662402274342573, 2: 0.963159759121502}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2wnwmursiUd",
        "outputId": "7240988d-040d-4d77-f47a-4e7d8b6bf914"
      },
      "source": [
        "basic_prep_cnn = create_basic_cnn(shape=INPUT_SHAPE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_2 (Conv2D)            (None, 126, 126, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 63, 63, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 61, 61, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 57600)             0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               7372928   \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 3)                 387       \n",
            "=================================================================\n",
            "Total params: 7,392,707\n",
            "Trainable params: 7,392,707\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPGZm4JMcae-"
      },
      "source": [
        ">Now that we've preprocessed our images, put the images into train, test, and validation folders, and created generators to feed into our model, we can now fit the model. Feel free to tweak the parameters of the three_callbacks() function. Make sure to read how the function works first if you choose to do so. Below are some recommendations for tweaking the function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "D6Z5zGOcmYCB",
        "outputId": "3ef66865-b8b0-401f-9f9a-31984d242e20"
      },
      "source": [
        "\"\"\"\r\n",
        "Recommended parameters to play around with:\r\n",
        " - epochs\r\n",
        " - lr_patience\r\n",
        " - factor\r\n",
        " - patience\r\n",
        " - restore_best_weights\r\n",
        "\r\n",
        "Please read the three_callbacks() function before changing!\r\n",
        "\r\n",
        "Note that: \r\n",
        " - patience should not be lower than lr_patience, otherwise learning rate decay\r\n",
        " will not occur.\r\n",
        " - Both patience and lr_patience should not be as large / larger than the number \r\n",
        " of epochs\r\n",
        "\"\"\"\r\n",
        "\r\n",
        "# Please define a filepath to store all models that will be saved from the \r\n",
        "# ModelCheckpoint callback in the variable 'cxr_model_storage'. \r\n",
        "cxr_model_storage = ensure_filepath('/cxr_models/')\r\n",
        "\r\n",
        "prep_hist = fit_plot_report_gen(basic_prep_cnn, cl_train_generator, \r\n",
        "                                cl_test_generator, cl_val_generator, \r\n",
        "                                epochs=10, batch_size=32,\r\n",
        "                                class_weights=class_weights_dict, \r\n",
        "                                callbacks=three_callbacks(lr_patience=3, factor=0.9, \r\n",
        "                                                          patience=5, \r\n",
        "                                                          restore_best_weights=False,\r\n",
        "                                                          f_path=cxr_model_storage))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "85/85 [==============================] - 27s 306ms/step - loss: 1.1528 - acc: 0.6505 - precision: 0.8603 - recall: 0.7471 - auc: 0.9249 - val_loss: 0.1590 - val_acc: 0.9433 - val_precision: 0.9456 - val_recall: 0.9407 - val_auc: 0.9933\n",
            "Epoch 2/10\n",
            "85/85 [==============================] - 25s 298ms/step - loss: 0.1377 - acc: 0.9527 - precision: 0.9578 - recall: 0.9496 - auc: 0.9945 - val_loss: 0.1038 - val_acc: 0.9691 - val_precision: 0.9690 - val_recall: 0.9665 - val_auc: 0.9970\n",
            "Epoch 3/10\n",
            "85/85 [==============================] - 25s 300ms/step - loss: 0.0960 - acc: 0.9691 - precision: 0.9701 - recall: 0.9658 - auc: 0.9968 - val_loss: 0.0996 - val_acc: 0.9639 - val_precision: 0.9639 - val_recall: 0.9639 - val_auc: 0.9966\n",
            "Epoch 4/10\n",
            "85/85 [==============================] - 25s 300ms/step - loss: 0.0628 - acc: 0.9789 - precision: 0.9789 - recall: 0.9788 - auc: 0.9988 - val_loss: 0.0960 - val_acc: 0.9665 - val_precision: 0.9665 - val_recall: 0.9665 - val_auc: 0.9974\n",
            "Epoch 5/10\n",
            "85/85 [==============================] - 25s 300ms/step - loss: 0.0451 - acc: 0.9860 - precision: 0.9869 - recall: 0.9855 - auc: 0.9992 - val_loss: 0.1109 - val_acc: 0.9639 - val_precision: 0.9639 - val_recall: 0.9639 - val_auc: 0.9953\n",
            "Epoch 6/10\n",
            "85/85 [==============================] - 26s 301ms/step - loss: 0.0183 - acc: 0.9959 - precision: 0.9959 - recall: 0.9959 - auc: 0.9999 - val_loss: 0.1150 - val_acc: 0.9613 - val_precision: 0.9613 - val_recall: 0.9613 - val_auc: 0.9963\n",
            "Epoch 7/10\n",
            "85/85 [==============================] - 25s 299ms/step - loss: 0.0201 - acc: 0.9956 - precision: 0.9956 - recall: 0.9949 - auc: 0.9999 - val_loss: 0.0935 - val_acc: 0.9768 - val_precision: 0.9768 - val_recall: 0.9768 - val_auc: 0.9955\n",
            "Epoch 8/10\n",
            "85/85 [==============================] - 26s 301ms/step - loss: 0.0036 - acc: 0.9995 - precision: 0.9995 - recall: 0.9995 - auc: 1.0000 - val_loss: 0.1039 - val_acc: 0.9665 - val_precision: 0.9665 - val_recall: 0.9665 - val_auc: 0.9953\n",
            "Epoch 9/10\n",
            "85/85 [==============================] - 26s 301ms/step - loss: 0.0025 - acc: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - val_loss: 0.1103 - val_acc: 0.9716 - val_precision: 0.9716 - val_recall: 0.9716 - val_auc: 0.9947\n",
            "Epoch 10/10\n",
            "85/85 [==============================] - 26s 301ms/step - loss: 0.0010 - acc: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - val_loss: 0.1193 - val_acc: 0.9742 - val_precision: 0.9742 - val_recall: 0.9742 - val_auc: 0.9954\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0009000000427477062.\n",
            "---------------------------------------------------------\n",
            "                  Classification Report\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.98       240\n",
            "           1       0.97      0.93      0.95       269\n",
            "           2       0.95      0.96      0.95       270\n",
            "\n",
            "    accuracy                           0.96       779\n",
            "   macro avg       0.96      0.96      0.96       779\n",
            "weighted avg       0.96      0.96      0.96       779\n",
            "\n",
            "---------------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------\n",
            "25/25 [==============================] - 6s 263ms/step - loss: 0.1763 - acc: 0.9602 - precision: 0.9602 - recall: 0.9589 - auc: 0.9903\n",
            "loss score: 0.17633983492851257\n",
            "accuracy score: 0.9602053761482239\n",
            "precision score: 0.9601542353630066\n",
            "recall score: 0.9589216709136963\n",
            "auc score: 0.9903229475021362\n",
            "\n",
            "Time to run cell: 272 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DEYObbsSp7w5"
      },
      "source": [
        "#### Viewing what the model sees using lime image explainer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "el13v9R2c261"
      },
      "source": [
        ">By using the lime library, we create a function that explains what the model determines to be important factors (both positive and negative) when diagnosing an image. Positive factors are marked with green, and negative factors are marked with red. \r\n",
        "\r\n",
        ">For more information about the lime library: https://lime-ml.readthedocs.io/en/latest/lime.html#module-lime.lime_image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439,
          "referenced_widgets": [
            "4c71f16091dd403b85111f17f18f5601",
            "7b5d47547d2145a682ee751efae505b8",
            "9968035c22f4425eb4079495985e8823",
            "1a736d9128f942168c8092ae754a8204",
            "57c41d7fc26f4a42bd215822fb8ac2cd",
            "ab5519fe073b495cabb347afe29a489a",
            "8ce43612d70b4ec888a9a3eeee63cc73",
            "2032f46a5d49411bad4d5e9487f562d7"
          ]
        },
        "id": "_3Cc0SemvWcx",
        "outputId": "36727f3a-cc2c-42b3-aeec-c5dbb6467b38"
      },
      "source": [
        "explain_image(basic_prep_cnn, cl_train_generator, num_samples=2000, num_feats=3, \r\n",
        "              class_label=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4c71f16091dd403b85111f17f18f5601",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYdd72tbuByY"
      },
      "source": [
        "### Saving best CXR models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QAOHIpwdQSo"
      },
      "source": [
        ">Now we define a filepath where we would like to save our best models. Please set a filepath for the 'best_models_filepath' variable where you would like to store your best models. Feel free to change the threshold, note that the lower the threshold, the more\r\n",
        "models will be saved to the 'best_models_filepath'. Models that do not surpass the threshold are removed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2tG2jWj9wy2",
        "outputId": "0c6bb4fb-8a50-4f77-eafd-df5deb42e97e"
      },
      "source": [
        "best_models_filepath = ensure_filepath('/content/drive/MyDrive/gdrive/models/capstone_models/cxr_models/')\r\n",
        "THRESHOLD = 0.96\r\n",
        "\r\n",
        "move_best_models(source=cxr_model_storage, threshold=THRESHOLD,\r\n",
        "                 new_dir=best_models_filepath, \r\n",
        "                 test_gen=cl_test_generator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25/25 [==============================] - 7s 269ms/step - loss: 0.1152 - acc: 0.9718 - precision: 0.9755 - recall: 0.9705 - auc: 0.9936\n",
            "model-03-0.1000.hdf5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qs9KLn_xdjTp"
      },
      "source": [
        ">To load in a saved model, please specify the filepath where you saved the model and put into the 'model_fpath' variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "QixDIrKMQV-V",
        "outputId": "3b3a302e-074e-4b0a-b791-f3dec44d7a9f"
      },
      "source": [
        "model_fpath = '/content/drive/MyDrive/gdrive/models/capstone_models/cxr_models/model-05-0.101.hdf5'\r\n",
        "\r\n",
        "best_model = load_model(model_fpath)\r\n",
        "class_report_gen(best_model, cl_test_generator, \r\n",
        "                 class_indices=test_generator.class_indices, cmap='Greens')\r\n",
        "best_model.evaluate(cl_test_generator, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---------------------------------------------------------\n",
            "                  Classification Report\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98       240\n",
            "           1       0.95      0.98      0.96       269\n",
            "           2       0.98      0.94      0.96       270\n",
            "\n",
            "    accuracy                           0.97       779\n",
            "   macro avg       0.97      0.97      0.97       779\n",
            "weighted avg       0.97      0.97      0.97       779\n",
            "\n",
            "---------------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "25/25 [==============================] - 7s 265ms/step - loss: 0.1188 - acc: 0.9679 - precision: 0.9704 - recall: 0.9679 - auc: 0.9941\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.11884350329637527,\n",
              " 0.9679075479507446,\n",
              " 0.9703989624977112,\n",
              " 0.9679075479507446,\n",
              " 0.994138777256012]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "cXAmdwp-j4r0",
        "outputId": "d21b54be-35cf-4fdd-ab22-403f01f03b1b"
      },
      "source": [
        "model_fpath = '/content/drive/MyDrive/gdrive/models/capstone_models/cxr_models/model-03-0.1000.hdf5'\r\n",
        "\r\n",
        "best_model2 = load_model(model_fpath)\r\n",
        "class_report_gen(best_model2, cl_test_generator, \r\n",
        "                 class_indices=test_generator.class_indices, cmap='Blues')\r\n",
        "best_model.evaluate(cl_test_generator, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---------------------------------------------------------\n",
            "                  Classification Report\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.99      0.98       240\n",
            "           1       0.96      0.97      0.97       269\n",
            "           2       0.98      0.95      0.97       270\n",
            "\n",
            "    accuracy                           0.97       779\n",
            "   macro avg       0.97      0.97      0.97       779\n",
            "weighted avg       0.97      0.97      0.97       779\n",
            "\n",
            "---------------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "25/25 [==============================] - 6s 263ms/step - loss: 0.1188 - acc: 0.9679 - precision: 0.9704 - recall: 0.9679 - auc: 0.9941\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.11884350329637527,\n",
              " 0.9679075479507446,\n",
              " 0.9703989624977112,\n",
              " 0.9679075479507446,\n",
              " 0.994138777256012]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iYHXNKwuEDoK"
      },
      "source": [
        "## Recommendation: \r\n",
        ">When working with images like x-rays or MRI scans, I highly recommend using CLAHE as a preprocessing technique to create new images that give the model more to learn from. CLAHE also is able to provide enough contrast to the image without overamplifying the intensity of the pixels. It is a great tool if the goal of your project involve detection and/or recognition."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BEEbwNmLkHt8"
      },
      "source": [
        "# If you are finished with the above section and would like to continue, please\r\n",
        "# set run = 1 to remove unecessary folders for Part 2\r\n",
        "ans = input(f\"All temporary files from Part 1 will be removed from the working \"\r\n",
        "            f\"directory. Proceed? [y][n] \")\r\n",
        "if ans.lower() == 'y':\r\n",
        "  print(\"Removing files\")\r\n",
        "  try:\r\n",
        "    shutil.rmtree(clahe_ttv_folder)\r\n",
        "    shutil.rmtree(cxr_model_storage)\r\n",
        "    print(\"Process completed\")\r\n",
        "  except:\r\n",
        "    print(\"No folders were removed\")\r\n",
        "else:\r\n",
        "  print(\"No folders were removed\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_I-xIJKwnqMi"
      },
      "source": [
        "# Part 1 Conclusion\r\n",
        ">The scores of our best model show an improvement in both recall rate (98%) for the COVID class and overall accuracy (97%). By using CLAHE, our model was able to reduce the amount of false negatives from our previous model by 4%. CLAHE is a great preprocessing tool that many hospitals and facilities should use when diagnosing their patients for certain diseases that may affect the targeted x-ray area.\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrrzBHKFoGvS"
      },
      "source": [
        "#### Caveats\r\n",
        ">While using x-rays to diagnose patients with COVID has proven to be successful with the model we've created, the process of each person getting an x-ray to determine if they have COVID can be costly. Furthermore, we wouldn't want someone with COVID to come into a facility and expose healthy people to the virus. In Part 2, we are going to explore the possiblities of using cough audio from healthy and COVID infected individuals and see if we can create a model that can accurately diagnose those with COVID. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDwRQCOCNZH8"
      },
      "source": [
        "## Part 2: Mel-Spectrogram Binary Classification\r\n",
        "\r\n",
        "Link to Part 2: [`covid_proj_p2_audio.ipynb`](https://colab.research.google.com/drive/1s4z-GJnNkE2NCx5MVccXxXzvBIc__tTX#scrollTo=WjwyDrj8S80W)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IvxVjydxZq0R"
      },
      "source": [
        "## References\r\n",
        "\r\n",
        "1. Virginia Department of Health. “COVID-19 Testing.” Vdh.Virginia.Gov, Virginia Department of Health, 5 Feb. 2021, www.vdh.virginia.gov/coronavirus/covid-19-testing/#:~:text=There%20are%20two%20main%20types,)%20and%20antibody%20tests.\r\n",
        "2. Shmerling, Robert H. “Which Test Is Best for COVID-19?” Harvard Health Blog, 5 Jan. 2021, www.health.harvard.edu/blog/which-test-is-best-for-covid-19-2020081020734#:%7E:text=The%20reported%20rate%20of%20false,infection%20the%20test%20is%20performed.\r\n",
        "3. “Coronavirus Disease 2019 (COVID-19).” Centers for Disease Control and Prevention, 11 Feb. 2020, www.cdc.gov/coronavirus/2019-ncov/more/scientific-brief-sars-cov-2.html#:%7E:text=%E2%80%A2%20Contact%20transmission%20is%20infection,(typically%20hours).\r\n",
        "4. Smyth, Tamara. “The Mel Scale.” University of California, San Diego, Tamara Smyth, 4 June 2019, musicweb.ucsd.edu/~trsmyth/pitch2/Mel_Scale.html.\r\n",
        "5. M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676.\r\n",
        "6. Lara Orlandic, Tomas Teijeiro, & David Atienza. (2020). The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4048312\r\n",
        "7. Chaudhari, Gunvant, et al. \"Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough.\" arXiv preprint arXiv:2011.13320 (2020).\r\n",
        "8. Hosseiny M, Kooraki S, Gholamrezanezhad A, Reddy S, Myers L. Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome. AJR Am J Roentgenol2020;214:1078-82. doi:10.2214/AJR.20.22969 pmid:32108495\r\n",
        "9. Zafra, Miguel Fernández. “Understanding Convolutions and Pooling in Neural Networks: A Simple Explanation.” Medium, 25 May 2020, towardsdatascience.com/understanding-convolutions-and-pooling-in-neural-networks-a-simple-explanation-885a2d78f211."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BOcXa7Qy1MgM"
      },
      "source": [
        "# Appendix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMdeGDwbhTgU"
      },
      "source": [
        "!pip install git+https://github.com/rcmalli/keras-squeezenet.git\r\n",
        "!pip install keras_applications==1.0.4 --no-deps"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2UF8fRp2Dz_"
      },
      "source": [
        "import tensorflow as tf\r\n",
        "\r\n",
        "import numpy as np\r\n",
        "import tensorflow.keras\r\n",
        "import \r\n",
        "from keras.applications.imagenet_utils import preprocess_input, decode_predictions\r\n",
        "from keras.preprocessing import image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcBtIp_f1RIs"
      },
      "source": [
        "import numpy as np\r\n",
        "import keras.applications.\r\n",
        "from keras_squeezenet import SqueezeNet\r\n",
        "from keras.applications.imagenet_utils import preprocess_input, decode_predictions\r\n",
        "from keras.preprocessing import image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LqMcLtt1sAm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}