Covid19Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import shutil\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.keras.preprocessing.image as img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture- CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "opti= keras.optimizers.Adam(lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model= keras.Sequential()\n",
    "model.add(Conv2D(input_shape=(224,224,3),activation='relu',filters=32,kernel_size=(3,3)))\n",
    "\n",
    "model.add(Conv2D(activation='relu',filters=64,kernel_size=(3,3)))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(activation='relu',filters=64,kernel_size=(3,3)))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(activation='relu',filters=128,kernel_size=(3,3)))\n",
    "model.add(MaxPooling2D(pool_size=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",
    "model.add(Dense(1,activation='sigmoid')) #binary classification\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "# increase the number of filters deeper in the model because receptive field increases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_16 (Conv2D)           (None, 222, 222, 32)      896       \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 220, 220, 64)      18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 110, 110, 64)      0         \n",
      "_________________________________________________________________\n",
      "dropout_16 (Dropout)         (None, 110, 110, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 108, 108, 64)      36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 54, 54, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_17 (Dropout)         (None, 54, 54, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 52, 52, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling (None, 26, 26, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_18 (Dropout)         (None, 26, 26, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 86528)             0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 64)                5537856   \n",
      "_________________________________________________________________\n",
      "dropout_19 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 5,668,097\n",
      "Trainable params: 5,668,097\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating data for the model - ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen= img.ImageDataGenerator(\n",
    "    rescale= 1/255.0,\n",
    "    shear_range= 0.2,\n",
    "    zoom_range= 0.2,\n",
    "    horizontal_flip=True\n",
    ")\n",
    "test_datagen= img.ImageDataGenerator(rescale=1/255.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 224 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(\n",
    "    r\"C:\\Users\\Ambika Sadhu\\Desktop\\covid chest\\CovidDataset\\Train\",\n",
    "    target_size=(224,224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Covid': 0, 'Normal': 1}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_generator.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 60 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "val_generator= test_datagen.flow_from_directory(\n",
    "    r\"C:\\Users\\Ambika Sadhu\\Desktop\\covid chest\\CovidDataset\\Val\",\n",
    "    target_size=(224,224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "7/7 [==============================] - 38s 5s/step - loss: 0.6926 - accuracy: 0.5000 - val_loss: 0.6908 - val_accuracy: 0.5000\n",
      "Epoch 2/10\n",
      "7/7 [==============================] - 36s 5s/step - loss: 0.6635 - accuracy: 0.6295 - val_loss: 0.6448 - val_accuracy: 0.8833\n",
      "Epoch 3/10\n",
      "7/7 [==============================] - 37s 5s/step - loss: 0.5731 - accuracy: 0.6964 - val_loss: 0.4832 - val_accuracy: 0.9333\n",
      "Epoch 4/10\n",
      "7/7 [==============================] - 38s 5s/step - loss: 0.4432 - accuracy: 0.7991 - val_loss: 0.3323 - val_accuracy: 0.9000\n",
      "Epoch 5/10\n",
      "7/7 [==============================] - 37s 5s/step - loss: 0.3655 - accuracy: 0.8348 - val_loss: 0.2413 - val_accuracy: 0.9500\n",
      "Epoch 6/10\n",
      "7/7 [==============================] - 37s 5s/step - loss: 0.2972 - accuracy: 0.8839 - val_loss: 0.1505 - val_accuracy: 0.9667\n",
      "Epoch 7/10\n",
      "7/7 [==============================] - 38s 5s/step - loss: 0.2342 - accuracy: 0.9286 - val_loss: 0.1269 - val_accuracy: 0.9667\n",
      "Epoch 8/10\n",
      "7/7 [==============================] - 38s 5s/step - loss: 0.1901 - accuracy: 0.9152 - val_loss: 0.1047 - val_accuracy: 0.9833\n",
      "Epoch 9/10\n",
      "7/7 [==============================] - 37s 5s/step - loss: 0.2356 - accuracy: 0.9241 - val_loss: 0.0780 - val_accuracy: 0.9667\n",
      "Epoch 10/10\n",
      "7/7 [==============================] - 37s 5s/step - loss: 0.1627 - accuracy: 0.9241 - val_loss: 0.0730 - val_accuracy: 0.9667\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator,steps_per_epoch=7,epochs=10,validation_data=val_generator,validation_steps=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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