PneumoniaDetectionModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Preprocessing of Images\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.layers import MaxPooling2D,Convolution2D,Dense,Dropout,Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5216 images belonging to 2 classes.\n",
      "Found 16 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen=ImageDataGenerator(\n",
    "                       rescale=1./255,\n",
    "                       shear_range=0.2,\n",
    "                       zoom_range=0.2,\n",
    "                       )\n",
    "validation_datagen=ImageDataGenerator(\n",
    "                       rescale=1./255,\n",
    "                       shear_range=0.2,\n",
    "                       zoom_range=0.2,\n",
    "                       )\n",
    "test_datagen=ImageDataGenerator(rescale=1./255)\n",
    "training_set=train_datagen.flow_from_directory(\n",
    "                                                './chest_xray/train',\n",
    "                                                 target_size=(150,150),batch_size=32,class_mode='binary')\n",
    "validation_set=train_datagen.flow_from_directory(\n",
    "                                                './chest_xray/val',\n",
    "                                                 target_size=(150,150\n",
    "                                                             ),batch_size=32,class_mode='binary')                              \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_4 (Conv2D)            (None, 146, 146, 32)      2432      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 73, 73, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 71, 71, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 35, 35, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 33, 33, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 32768)             0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 512)               16777728  \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 16,938,305\n",
      "Trainable params: 16,938,305\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=Sequential()\n",
    "model.add(Convolution2D(32,(5,5),activation='relu',input_shape=(150,150,3)))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(Convolution2D(64,(3,3),activation='relu',input_shape=(150,150,3)))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(Convolution2D(128,(3,3),activation='relu',input_shape=(150,150,3)))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512,activation='relu'))\n",
    "model.add(Dense(128,activation='relu'))\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1000/1000 [==============================] - 959s 959ms/step - loss: 0.2180 - accuracy: 0.9121 - val_loss: 1.6283 - val_accuracy: 0.6000\n",
      "Epoch 2/5\n",
      "1000/1000 [==============================] - 893s 893ms/step - loss: 0.1251 - accuracy: 0.9522 - val_loss: 0.5672 - val_accuracy: 0.7044\n",
      "Epoch 3/5\n",
      "1000/1000 [==============================] - 887s 887ms/step - loss: 0.0971 - accuracy: 0.9630 - val_loss: 0.4769 - val_accuracy: 0.7669\n",
      "Epoch 4/5\n",
      "1000/1000 [==============================] - 917s 917ms/step - loss: 0.0854 - accuracy: 0.9686 - val_loss: 0.1269 - val_accuracy: 0.8100\n",
      "Epoch 5/5\n",
      "1000/1000 [==============================] - 931s 931ms/step - loss: 0.0676 - accuracy: 0.9741 - val_loss: 0.5806 - val_accuracy: 0.8719\n"
     ]
    }
   ],
   "source": [
    "hist=model.fit_generator(training_set,steps_per_epoch=1000,epochs=5,validation_data=validation_set,validation_steps=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "plt.figure(0)\n",
    "plt.plot(hist.history['loss'],'g',label='Training Loss')\n",
    "plt.plot(hist.history['val_loss'],'b',label='Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "plt.figure(0)\n",
    "plt.plot(hist.history['accuracy'],'g',label='Training Acc')\n",
    "plt.plot(hist.history['val_accuracy'],'b',label='Validation Acc')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}