Covid-19 Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "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": {},
   "source": [
    "#  Model deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining paths\n",
    "TRAIN_PATH = \"CovidDataset/Train\"\n",
    "VAL_PATH = \"CovidDataset/Test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training model\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(224,224,3)))\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(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(Flatten())\n",
    "model.add(Dense(64,activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "\n",
    "model.compile(loss=keras.losses.binary_crossentropy,optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_9 (Conv2D)            (None, 222, 222, 32)      896       \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 220, 220, 128)     36992     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 110, 110, 128)     0         \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 110, 110, 128)     0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 108, 108, 64)      73792     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 54, 54, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 54, 54, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 52, 52, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 26, 26, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 26, 26, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 86528)             0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 64)                5537856   \n",
      "_________________________________________________________________\n",
      "dropout_12 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 5,723,457\n",
      "Trainable params: 5,723,457\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Getting parameters\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Moulding train images\n",
    "train_datagen = image.ImageDataGenerator(rescale = 1./255, shear_range = 0.2,zoom_range = 0.2, horizontal_flip = True)\n",
    "\n",
    "test_dataset = image.ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 224 images belonging to 2 classes.\n",
      "Found 60 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "#Reshaping test and validation images \n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    'CovidDataset/Train',\n",
    "    target_size = (224,224),\n",
    "    batch_size = 32,\n",
    "    class_mode = 'binary')\n",
    "validation_generator = test_dataset.flow_from_directory(\n",
    "    'CovidDataset/Val',\n",
    "    target_size = (224,224),\n",
    "    batch_size = 32,\n",
    "    class_mode = 'binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "8/8 [==============================] - 87s 11s/step - loss: 0.9859 - accuracy: 0.5586 - val_loss: 0.6590 - val_accuracy: 0.5000\n",
      "Epoch 2/10\n",
      "8/8 [==============================] - 85s 11s/step - loss: 0.5413 - accuracy: 0.7031 - val_loss: 0.4960 - val_accuracy: 0.7333\n",
      "Epoch 3/10\n",
      "8/8 [==============================] - 81s 10s/step - loss: 0.4355 - accuracy: 0.8086 - val_loss: 0.5215 - val_accuracy: 0.8833\n",
      "Epoch 4/10\n",
      "8/8 [==============================] - 83s 10s/step - loss: 0.3409 - accuracy: 0.8867 - val_loss: 0.1301 - val_accuracy: 0.9333\n",
      "Epoch 5/10\n",
      "8/8 [==============================] - 79s 10s/step - loss: 0.1877 - accuracy: 0.9297 - val_loss: 0.1835 - val_accuracy: 0.9000\n",
      "Epoch 6/10\n",
      "8/8 [==============================] - 85s 11s/step - loss: 0.1891 - accuracy: 0.9453 - val_loss: 0.0865 - val_accuracy: 0.9667\n",
      "Epoch 7/10\n",
      "8/8 [==============================] - 84s 11s/step - loss: 0.1769 - accuracy: 0.9219 - val_loss: 0.1316 - val_accuracy: 0.9667\n",
      "Epoch 8/10\n",
      "8/8 [==============================] - 80s 10s/step - loss: 0.1663 - accuracy: 0.9492 - val_loss: 0.0839 - val_accuracy: 0.9833\n",
      "Epoch 9/10\n",
      "8/8 [==============================] - 90s 11s/step - loss: 0.1281 - accuracy: 0.9648 - val_loss: 0.0524 - val_accuracy: 0.9833\n",
      "Epoch 10/10\n",
      "8/8 [==============================] - 95s 12s/step - loss: 0.1501 - accuracy: 0.9453 - val_loss: 0.0668 - val_accuracy: 0.9667\n"
     ]
    }
   ],
   "source": [
    "#Training the model\n",
    "hist_new = 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": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'val_loss': [5.476156711578369, 8.214235305786133, 6.571388244628906, 9.857081413269043, 5.476156711578369, 8.214235305786133, 7.119003772735596, 6.023772716522217, 8.214235305786133, 6.023772716522217], 'val_accuracy': [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 'loss': [7.846305787563324, 7.546828627586365, 7.786410331726074, 7.486932873725891, 7.54682856798172, 7.966096818447113, 7.486933052539825, 7.906201183795929, 7.367141962051392, 7.8463059067726135], 'accuracy': [0.48828125, 0.5078125, 0.4921875, 0.51171875, 0.5078125, 0.48046875, 0.51171875, 0.484375, 0.51953125, 0.48828125]}\n"
     ]
    }
   ],
   "source": [
    "#Getting summary\n",
    "summary=hist.history\n",
    "print(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"model_covid.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.11957968026399612, 0.9642857313156128]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate_generator(train_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.034558333456516266, 0.9666666388511658]\n"
     ]
    }
   ],
   "source": [
    "print(model.evaluate_generator(validation_generator))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Covid': 0, 'Normal': 1}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "train_generator.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_actual, y_test = [],[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in os.listdir(\"./CovidDataset/Val/Normal/\"):\n",
    "    img=image.load_img(\"./CovidDataset/Val/Normal/\"+i,target_size=(224,224))\n",
    "    img=image.img_to_array(img)\n",
    "    img=np.expand_dims(img,axis=0)\n",
    "    pred=model.predict_classes(img)\n",
    "    y_test.append(pred[0,0])\n",
    "    y_actual.append(1)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in os.listdir(\"./CovidDataset/Val/Covid/\"):\n",
    "    img=image.load_img(\"./CovidDataset/Val/Covid/\"+i,target_size=(224,224))\n",
    "    img=image.img_to_array(img)\n",
    "    img=np.expand_dims(img,axis=0)\n",
    "    pred=model.predict_classes(img)\n",
    "    y_test.append(pred[0,0])\n",
    "    y_actual.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_actual=np.array(y_actual)\n",
    "y_test=np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "cn=confusion_matrix(y_actual,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x182bdbc07f0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.heatmap(cn,cmap=\"plasma\",annot=True) #0: Covid ; 1: Normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}