Final_Covid_19_Detection_on_XRAYimages.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the data for positive samples \n",
    "\n",
    "FILE_PATH = \"covid-chestxray-dataset-master\\metadata.csv\"\n",
    "IMAGE_PATH = \"covid-chestxray-dataset-master\\images\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360, 28)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(FILE_PATH)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>patientid</th>\n",
       "      <th>offset</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>finding</th>\n",
       "      <th>survival</th>\n",
       "      <th>intubated</th>\n",
       "      <th>intubation_present</th>\n",
       "      <th>went_icu</th>\n",
       "      <th>needed_supplemental_O2</th>\n",
       "      <th>...</th>\n",
       "      <th>date</th>\n",
       "      <th>location</th>\n",
       "      <th>folder</th>\n",
       "      <th>filename</th>\n",
       "      <th>doi</th>\n",
       "      <th>url</th>\n",
       "      <th>license</th>\n",
       "      <th>clinical_notes</th>\n",
       "      <th>other_notes</th>\n",
       "      <th>Unnamed: 27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>COVID-19</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>January 22, 2020</td>\n",
       "      <td>Cho Ray Hospital, Ho Chi Minh City, Vietnam</td>\n",
       "      <td>images</td>\n",
       "      <td>auntminnie-a-2020_01_28_23_51_6665_2020_01_28_...</td>\n",
       "      <td>10.1056/nejmc2001272</td>\n",
       "      <td>https://www.nejm.org/doi/full/10.1056/NEJMc200...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>infiltrate in the upper lobe of the left lung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>COVID-19</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>January 25, 2020</td>\n",
       "      <td>Cho Ray Hospital, Ho Chi Minh City, Vietnam</td>\n",
       "      <td>images</td>\n",
       "      <td>auntminnie-b-2020_01_28_23_51_6665_2020_01_28_...</td>\n",
       "      <td>10.1056/nejmc2001272</td>\n",
       "      <td>https://www.nejm.org/doi/full/10.1056/NEJMc200...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive infiltrate and consolidation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>COVID-19</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>January 27, 2020</td>\n",
       "      <td>Cho Ray Hospital, Ho Chi Minh City, Vietnam</td>\n",
       "      <td>images</td>\n",
       "      <td>auntminnie-c-2020_01_28_23_51_6665_2020_01_28_...</td>\n",
       "      <td>10.1056/nejmc2001272</td>\n",
       "      <td>https://www.nejm.org/doi/full/10.1056/NEJMc200...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive infiltrate and consolidation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>COVID-19</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>January 28, 2020</td>\n",
       "      <td>Cho Ray Hospital, Ho Chi Minh City, Vietnam</td>\n",
       "      <td>images</td>\n",
       "      <td>auntminnie-d-2020_01_28_23_51_6665_2020_01_28_...</td>\n",
       "      <td>10.1056/nejmc2001272</td>\n",
       "      <td>https://www.nejm.org/doi/full/10.1056/NEJMc200...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive infiltrate and consolidation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>52.0</td>\n",
       "      <td>COVID-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>January 25, 2020</td>\n",
       "      <td>Changhua Christian Hospital, Changhua City, Ta...</td>\n",
       "      <td>images</td>\n",
       "      <td>nejmc2001573_f1a.jpeg</td>\n",
       "      <td>10.1056/NEJMc2001573</td>\n",
       "      <td>https://www.nejm.org/doi/full/10.1056/NEJMc200...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>diffuse infiltrates in the bilateral lower lungs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   patientid  offset sex   age   finding survival intubated  \\\n",
       "0          2     0.0   M  65.0  COVID-19        Y       NaN   \n",
       "1          2     3.0   M  65.0  COVID-19        Y       NaN   \n",
       "2          2     5.0   M  65.0  COVID-19        Y       NaN   \n",
       "3          2     6.0   M  65.0  COVID-19        Y       NaN   \n",
       "4          4     0.0   F  52.0  COVID-19      NaN       NaN   \n",
       "\n",
       "  intubation_present went_icu needed_supplemental_O2  ...              date  \\\n",
       "0                NaN      NaN                    NaN  ...  January 22, 2020   \n",
       "1                NaN      NaN                    NaN  ...  January 25, 2020   \n",
       "2                NaN      NaN                    NaN  ...  January 27, 2020   \n",
       "3                NaN      NaN                    NaN  ...  January 28, 2020   \n",
       "4                NaN      NaN                    NaN  ...  January 25, 2020   \n",
       "\n",
       "                                            location  folder  \\\n",
       "0        Cho Ray Hospital, Ho Chi Minh City, Vietnam  images   \n",
       "1        Cho Ray Hospital, Ho Chi Minh City, Vietnam  images   \n",
       "2        Cho Ray Hospital, Ho Chi Minh City, Vietnam  images   \n",
       "3        Cho Ray Hospital, Ho Chi Minh City, Vietnam  images   \n",
       "4  Changhua Christian Hospital, Changhua City, Ta...  images   \n",
       "\n",
       "                                            filename                   doi  \\\n",
       "0  auntminnie-a-2020_01_28_23_51_6665_2020_01_28_...  10.1056/nejmc2001272   \n",
       "1  auntminnie-b-2020_01_28_23_51_6665_2020_01_28_...  10.1056/nejmc2001272   \n",
       "2  auntminnie-c-2020_01_28_23_51_6665_2020_01_28_...  10.1056/nejmc2001272   \n",
       "3  auntminnie-d-2020_01_28_23_51_6665_2020_01_28_...  10.1056/nejmc2001272   \n",
       "4                              nejmc2001573_f1a.jpeg  10.1056/NEJMc2001573   \n",
       "\n",
       "                                                 url license  \\\n",
       "0  https://www.nejm.org/doi/full/10.1056/NEJMc200...     NaN   \n",
       "1  https://www.nejm.org/doi/full/10.1056/NEJMc200...     NaN   \n",
       "2  https://www.nejm.org/doi/full/10.1056/NEJMc200...     NaN   \n",
       "3  https://www.nejm.org/doi/full/10.1056/NEJMc200...     NaN   \n",
       "4  https://www.nejm.org/doi/full/10.1056/NEJMc200...     NaN   \n",
       "\n",
       "                                     clinical_notes other_notes Unnamed: 27  \n",
       "0     infiltrate in the upper lobe of the left lung         NaN         NaN  \n",
       "1          progressive infiltrate and consolidation         NaN         NaN  \n",
       "2          progressive infiltrate and consolidation         NaN         NaN  \n",
       "3          progressive infiltrate and consolidation         NaN         NaN  \n",
       "4  diffuse infiltrates in the bilateral lower lungs         NaN         NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covid FOlder Created\n"
     ]
    }
   ],
   "source": [
    "TARGET_DIR = \"Dataset/Train/Covid\"\n",
    "\n",
    "if not os.path.exists(TARGET_DIR):\n",
    "    os.mkdir(TARGET_DIR)\n",
    "    print(\"Covid FOlder Created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copy COVID-19 images with view point PA from Downloaded directory to Target Directory\n",
    "cnt = 0\n",
    "for(i,row) in df.iterrows():\n",
    "    if row[\"finding\"] == \"COVID-19\" and row[\"view\"] ==\"PA\":\n",
    "        filename = row[\"filename\"]\n",
    "        image_path = os.path.join(IMAGE_PATH,filename) #IMAGE_PATH + filename\n",
    "        image_copy_path = os.path.join(TARGET_DIR,filename) #TARGET_DIR + filename\n",
    "        shutil.copy2(image_path,image_copy_path) #Copy From IMAGE_PATH to TARGET_DIrectory\n",
    "       # print(\"Moving Image\",cnt)\n",
    "        cnt+=1\n",
    "# print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Folder Created\n"
     ]
    }
   ],
   "source": [
    "# Sampling of Images from Kaggle Data,As we have 143 around covid images so we will have 143 around normal images of xray\n",
    "\n",
    "import random\n",
    "KAGGLE_FILE_PATH = \"chest_xray\\\\train\\\\NORMAL\"\n",
    "TARGET_NORMAL_DIR = \"Dataset\\\\Train\\\\Normal\"\n",
    "if not os.path.exists(TARGET_NORMAL_DIR):\n",
    "    os.mkdir(TARGET_NORMAL_DIR)\n",
    "    print(\"Normal Folder Created\")\n",
    "    \n",
    "image_names = os.listdir(KAGGLE_FILE_PATH) #COntains list of all image names\n",
    "# images_names\n",
    "random.shuffle(image_names) #it will randomly shuffle names in list\n",
    "\n",
    "for i in range(144):\n",
    "    image_name = image_names[i]\n",
    "    image_path = os.path.join(KAGGLE_FILE_PATH,image_name)\n",
    "    \n",
    "    target_path = os.path.join(TARGET_NORMAL_DIR,image_name)\n",
    "    shutil.copy2(image_path,target_path)\n",
    "#     print(\"Moved\",i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8F7z14PADOiw"
   },
   "outputs": [],
   "source": [
    "TRAIN_PATH = \"Dataset/Train\"\n",
    "VAL_PATH = \"Dataset/Val\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Importing Required Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kLbMjKaGEDSX",
    "outputId": "960a05c1-13f1-44d1-c41b-4f37e6798160"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "from keras.layers import *\n",
    "from keras.models import *\n",
    "from keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zFsK_XFhEZwP"
   },
   "source": [
    "# **Building Architecture**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nMcRnjckES5E"
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32,kernel_size=(3,3),activation=\"relu\",input_shape=(224,224,3)))\n",
    "\n",
    "model.add(Conv2D(64,(3,3),activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(64,(3,3),activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(128,(3,3),activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(128,(3,3),activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64,activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1,activation=\"sigmoid\"))\n",
    "\n",
    "model.compile(loss=keras.losses.binary_crossentropy,optimizer = \"adam\",metrics=[\"accuracy\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 756
    },
    "colab_type": "code",
    "id": "k81GqhjJIDnE",
    "outputId": "13d932ee-48e9-46c5-abd0-1b340797c2f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 222, 222, 32)      896       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 220, 220, 64)      18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 110, 110, 64)      0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 110, 110, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 108, 108, 64)      36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 54, 54, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 54, 54, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 52, 52, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 26, 26, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 26, 26, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 24, 24, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 18432)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                1179712   \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 1,457,537\n",
      "Trainable params: 1,457,537\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1UIwIT95JKDU"
   },
   "source": [
    "# **Train From Scratch**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Data Augmentation*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lt8si8MKIaUM"
   },
   "outputs": [],
   "source": [
    "train_datagen = image.ImageDataGenerator(\n",
    "    rescale = 1./255,\n",
    "    shear_range = 0.2,\n",
    "    zoom_range = 0.2,\n",
    "    horizontal_flip = True,\n",
    ")\n",
    "test_dataset = image.ImageDataGenerator(rescale = 1./255)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ikdogu6GJ06S",
    "outputId": "beb15015-2377-49a9-ac80-fbb371215754"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 288 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(\n",
    "    'Dataset/Train',\n",
    "    target_size = (224,224),\n",
    "    batch_size = 32,\n",
    "    class_mode = 'binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "j37uuWU3KPM4",
    "outputId": "b804a6d6-6107-4cd2-e3cf-6a43787f2649"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Covid': 0, 'Normal': 1}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_generator.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "eln76WRWKYvB",
    "outputId": "4c782a1d-25af-4b55-b235-464b65b2b4a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 60 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_generator = test_dataset.flow_from_directory(\n",
    "    'Dataset/Val',\n",
    "    target_size = (224,224),\n",
    "    batch_size = 32,\n",
    "    class_mode = 'binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Fit The Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "colab_type": "code",
    "id": "bJD_HDI3LOML",
    "outputId": "5d2a1fb5-aa88-44ca-ca27-bda380cf421e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "8/8 [==============================] - 144s 18s/step - loss: 0.9547 - acc: 0.5195 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 2/10\n",
      "8/8 [==============================] - 84s 11s/step - loss: 0.6900 - acc: 0.5430 - val_loss: 0.6845 - val_acc: 0.8833\n",
      "Epoch 3/10\n",
      "8/8 [==============================] - 83s 10s/step - loss: 0.6301 - acc: 0.7031 - val_loss: 0.5778 - val_acc: 0.8500\n",
      "Epoch 4/10\n",
      "8/8 [==============================] - 83s 10s/step - loss: 0.5882 - acc: 0.6758 - val_loss: 0.5669 - val_acc: 0.8500\n",
      "Epoch 5/10\n",
      "8/8 [==============================] - 84s 11s/step - loss: 0.4291 - acc: 0.8203 - val_loss: 0.2348 - val_acc: 0.9667\n",
      "Epoch 6/10\n",
      "8/8 [==============================] - 83s 10s/step - loss: 0.3038 - acc: 0.8789 - val_loss: 0.2660 - val_acc: 0.9833\n",
      "Epoch 7/10\n",
      "8/8 [==============================] - 82s 10s/step - loss: 0.1850 - acc: 0.9297 - val_loss: 0.0565 - val_acc: 0.9833\n",
      "Epoch 8/10\n",
      "8/8 [==============================] - 83s 10s/step - loss: 0.2077 - acc: 0.9414 - val_loss: 0.0763 - val_acc: 0.9833\n",
      "Epoch 9/10\n",
      "8/8 [==============================] - 84s 10s/step - loss: 0.1727 - acc: 0.9375 - val_loss: 0.0788 - val_acc: 0.9833\n",
      "Epoch 10/10\n",
      "8/8 [==============================] - 82s 10s/step - loss: 0.1920 - acc: 0.9570 - val_loss: 0.1905 - val_acc: 0.9833\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch = 8,\n",
    "    epochs = 10,\n",
    "    validation_data = validation_generator,\n",
    "    validation_steps = 2\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F109fnZmV42P"
   },
   "source": [
    "## *Loss is very less and accuracy is on point*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xGcBDThIL-p8"
   },
   "outputs": [],
   "source": [
    "model.save(\"Detection_Covid_19.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "qEfEw0JgVhyB",
    "outputId": "0d119926-68d5-4a20-acee-71d06dcdffa4"
   },
   "outputs": [],
   "source": [
    "# model.evaluate_generator(train_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "i5l_dGt6VmzI",
    "outputId": "e3d0412e-c528-4395-ed92-589289a73445"
   },
   "outputs": [],
   "source": [
    "# model.evaluate_generator(validation_generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BPVdV0CGV_Tp"
   },
   "source": [
    "# **Test Images**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WpINfswbVyM_"
   },
   "outputs": [],
   "source": [
    "model = load_model(\"Detection_Covid_19.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EqZ6D07_WRNE"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "RgtgamSQWUZj",
    "outputId": "a644c086-0ba5-4ee5-f38d-c85341a05bfa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Covid': 0, 'Normal': 1}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_generator.class_indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8WtXLdRLWXed"
   },
   "outputs": [],
   "source": [
    "y_actual = []\n",
    "y_test = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WW1UC4qrWacp"
   },
   "outputs": [],
   "source": [
    "for i in os.listdir(\"./Dataset/Val/Normal\"):\n",
    "  img = image.load_img(\"./Dataset/Val/Normal/\"+i,target_size=(224,224))\n",
    "  img = image.img_to_array(img)\n",
    "  img = np.expand_dims(img,axis=0)\n",
    "  p = model.predict_classes(img)\n",
    "  y_test.append(p[0,0])\n",
    "  y_actual.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XwqD0RAKW5ck"
   },
   "outputs": [],
   "source": [
    "for i in os.listdir(\"./Dataset/Val/Covid\"):\n",
    "  img = image.load_img(\"./Dataset/Val/Covid/\"+i,target_size=(224,224))\n",
    "  img = image.img_to_array(img)\n",
    "  img = np.expand_dims(img,axis=0)\n",
    "  p = model.predict_classes(img)\n",
    "  y_test.append(p[0,0])\n",
    "  y_actual.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5NuoneYpXEd7"
   },
   "outputs": [],
   "source": [
    "y_actual = np.array(y_actual)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nv58xeJfXKq9"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BlRsMa76XQkh"
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_actual,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FliAEEsVXWac"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "Ww0pt8kKXaCE",
    "outputId": "3ad9e00d-8810-47cc-bf93-749710ab8851",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# sns.heatmap(cm,cmap = \"plasma\" , annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "class_names = [\"Covid-19\",\"Normal\"]\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=\"plasma\"):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Confusion Matrix***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[30  0]\n",
      " [ 1 29]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plot_confusion_matrix(cm, classes=class_names,\n",
    "                      title='Confusion matrix for Covid-19 Detection',cmap=\"plasma\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **List all data in history**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "history = hist\n",
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Summarize history for accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Summarize history for loss**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9jXc3qtyc-iv"
   },
   "source": [
    "# **Predictions from X-Ray Images**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "colab_type": "code",
    "id": "nnRGu4QHXhSZ",
    "outputId": "8d19ae21-ad78-4e33-d777-d8868d5d738b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This Xray Image is of positive covid-19 patient\n",
      "Prediction Of Our Model :  Positive For Covid-19\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# from google.colab.patches import cv2_imshow\n",
    "import cv2\n",
    "from keras.preprocessing import image\n",
    "xtest_image = image.load_img('Dataset/Prediction/ryct.2020200034.fig5-day7.jpeg', target_size = (224, 224))\n",
    "xtest_image = image.img_to_array(xtest_image)\n",
    "xtest_image = np.expand_dims(xtest_image, axis = 0)\n",
    "results = model.predict_classes(xtest_image)\n",
    "# training_set.class_indices\n",
    "imggg = cv2.imread('Dataset/Prediction/ryct.2020200034.fig5-day7.jpeg')\n",
    "print(\"This Xray Image is of positive covid-19 patient\")\n",
    "imggg = np.array(imggg)\n",
    "imggg = cv2.resize(imggg,(400,400))\n",
    "plt.imshow(imggg)\n",
    "# cv2_imshow(imggg)\n",
    "# print(results)\n",
    "if results[0][0] == 0:\n",
    "    prediction = 'Positive For Covid-19'\n",
    "else:\n",
    "    prediction = 'Negative for Covid-19'\n",
    "print(\"Prediction Of Our Model : \",prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "colab_type": "code",
    "id": "NWXNaZpWZue6",
    "outputId": "a04a473a-1553-48d6-c301-1f1c1477647f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This Xray Image is of Negative covid-19 patient\n",
      "Prediction Of Our Model :  Negative for Covid-19\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# from google.colab.patches import cv2_imshow\n",
    "from keras.preprocessing import image\n",
    "xtest_image = image.load_img('Dataset/Prediction/NORMAL2-IM-0354-0001.jpeg', target_size = (224, 224))\n",
    "xtest_image = image.img_to_array(xtest_image)\n",
    "xtest_image = np.expand_dims(xtest_image, axis = 0)\n",
    "results = model.predict_classes(xtest_image)\n",
    "# training_set.class_indices\n",
    "\n",
    "imggg = cv2.imread('Dataset/Prediction/NORMAL2-IM-0354-0001.jpeg')\n",
    "print(\"This Xray Image is of Negative covid-19 patient\")\n",
    "imggg = np.array(imggg)\n",
    "imggg = cv2.resize(imggg,(400,400))\n",
    "\n",
    "plt.imshow(imggg)\n",
    "# cv2_imshow(imggg)\n",
    "# print(results)\n",
    "if results[0][0] == 0:\n",
    "    prediction = 'Positive For Covid-19'\n",
    "else:\n",
    "    prediction = 'Negative for Covid-19'\n",
    "print(\"Prediction Of Our Model : \",prediction)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Covid-19_Detection_on_XRAYimages.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}