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--- a +++ b/Pneumonia_Detection.ipynb @@ -0,0 +1,1307 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "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": "3.7.6" + }, + "colab": { + "name": "Pneumonia Detection.ipynb", + "provenance": [], + "include_colab_link": true + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "<a href=\"https://colab.research.google.com/github/AnkitRajSri/Pneumonia-Detection/blob/master/Pneumonia_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jETrabyQwLW2", + "colab_type": "text" + }, + "source": [ + "### Importing the libraries" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "U9qWWT1ewLW6", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import os\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "from keras.preprocessing import image\n", + "from keras.preprocessing.image import ImageDataGenerator" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "9pmdklbswLXK", + "colab_type": "code", + "colab": {}, + "outputId": "f9325e42-749a-48fe-ced1-7268622bee57" + }, + "source": [ + "tf.__version__" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LuEvwqwAwLXb", + "colab_type": "text" + }, + "source": [ + "### Data Preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KxN_t-26wLXd", + "colab_type": "text" + }, + "source": [ + "##### Preprocessing the training set" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "k8yFRQyCwLXf", + "colab_type": "code", + "colab": {}, + "outputId": "ad0da542-220e-4857-9927-fdf2394b37e8" + }, + "source": [ + "train_datagen = ImageDataGenerator(\n", + " rescale=1./255,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True)\n", + "\n", + "training_set = train_datagen.flow_from_directory(\n", + " 'train', target_size=(64, 64),\n", + " batch_size=32,\n", + " class_mode='binary')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Found 5216 images belonging to 2 classes.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "F7A1jcC-wLXr", + "colab_type": "code", + "colab": {}, + "outputId": "eaebfab0-3fff-46f9-8977-beff4c57259a" + }, + "source": [ + "validation_datagen = ImageDataGenerator(rescale=1./255)\n", + "\n", + "validation_set = validation_datagen.flow_from_directory(\n", + " 'val', target_size=(64, 64),\n", + " batch_size=32,\n", + " class_mode='binary')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Found 16 images belonging to 2 classes.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "G-tm781fwLX2", + "colab_type": "code", + "colab": {}, + "outputId": "2516ae5b-ab78-4ddb-db08-3db7cec47f38" + }, + "source": [ + "test_datagen = ImageDataGenerator(rescale=1./255)\n", + "\n", + "test_set = test_datagen.flow_from_directory(\n", + " 'test', target_size=(64, 64),\n", + " batch_size=32,\n", + " class_mode='binary')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Found 624 images belonging to 2 classes.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P-AsgjL5wLYA", + "colab_type": "text" + }, + "source": [ + "### Building the CNN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Gs6izkQawLYC", + "colab_type": "text" + }, + "source": [ + "##### Initializing the CNN and adding the layers" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qhRn_PPMwLYD", + "colab_type": "code", + "colab": {} + }, + "source": [ + "cnn = tf.keras.Sequential()\n", + "\n", + "cnn.add(tf.keras.layers.Conv2D(filters=32, \n", + " kernel_size=3,\n", + " activation='relu',\n", + " input_shape=[64, 64, 3]))\n", + "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n", + "cnn.add(tf.keras.layers.Conv2D(filters=32, \n", + " kernel_size=3,\n", + " activation='relu'))\n", + "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n", + "cnn.add(tf.keras.layers.Flatten())\n", + "cnn.add(tf.keras.layers.Dense(units=256, activation='relu'))\n", + "cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dtzJ9tbqwLYS", + "colab_type": "text" + }, + "source": [ + "#### Compiling the CNN" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EL6XoP-pwLYU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "cnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EkeukAK2wLYh", + "colab_type": "text" + }, + "source": [ + "##### Training the CNN on the training set and evaluating it on the validation set" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BAKUV9CywLYj", + "colab_type": "code", + "colab": {}, + "outputId": "9432f8a2-8939-40c5-b2ca-d5c057b4e777" + }, + "source": [ + "cnn.fit(x=training_set, validation_data=test_set, epochs=25)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "Train for 163 steps, validate for 20 steps\n", + "Epoch 1/25\n", + "163/163 [==============================] - 119s 731ms/step - loss: 0.0716 - accuracy: 0.9730 - val_loss: 0.2369 - val_accuracy: 0.9311\n", + "Epoch 2/25\n", + "163/163 [==============================] - 124s 761ms/step - loss: 0.0677 - accuracy: 0.9755 - val_loss: 0.4190 - val_accuracy: 0.8974\n", + "Epoch 3/25\n", + "163/163 [==============================] - 119s 732ms/step - loss: 0.0667 - accuracy: 0.9747 - val_loss: 0.3130 - val_accuracy: 0.9054\n", + "Epoch 4/25\n", + "163/163 [==============================] - 120s 738ms/step - loss: 0.0571 - accuracy: 0.9793 - val_loss: 0.3345 - val_accuracy: 0.8958\n", + "Epoch 5/25\n", + "163/163 [==============================] - 118s 725ms/step - loss: 0.0593 - accuracy: 0.9760 - val_loss: 0.2856 - val_accuracy: 0.9151\n", + "Epoch 6/25\n", + "163/163 [==============================] - 108s 665ms/step - loss: 0.0584 - accuracy: 0.9799 - val_loss: 0.2743 - val_accuracy: 0.9263\n", + "Epoch 7/25\n", + "163/163 [==============================] - 108s 662ms/step - loss: 0.0560 - accuracy: 0.9791 - val_loss: 0.2714 - val_accuracy: 0.9087\n", + "Epoch 8/25\n", + "163/163 [==============================] - 113s 693ms/step - loss: 0.0550 - accuracy: 0.9793 - val_loss: 0.4636 - val_accuracy: 0.8734\n", + "Epoch 9/25\n", + "163/163 [==============================] - 128s 788ms/step - loss: 0.0581 - accuracy: 0.9789 - val_loss: 0.2477 - val_accuracy: 0.9151\n", + "Epoch 10/25\n", + "163/163 [==============================] - 117s 719ms/step - loss: 0.0547 - accuracy: 0.9812 - val_loss: 0.2690 - val_accuracy: 0.9151\n", + "Epoch 11/25\n", + "163/163 [==============================] - 110s 678ms/step - loss: 0.0546 - accuracy: 0.9810 - val_loss: 0.2803 - val_accuracy: 0.9167\n", + "Epoch 12/25\n", + "163/163 [==============================] - 113s 693ms/step - loss: 0.0574 - accuracy: 0.9776 - val_loss: 0.4011 - val_accuracy: 0.8830\n", + "Epoch 13/25\n", + "163/163 [==============================] - 110s 677ms/step - loss: 0.0516 - accuracy: 0.9816 - val_loss: 0.2724 - val_accuracy: 0.9263\n", + "Epoch 14/25\n", + "163/163 [==============================] - 121s 744ms/step - loss: 0.0463 - accuracy: 0.9833 - val_loss: 0.3101 - val_accuracy: 0.9183\n", + "Epoch 15/25\n", + "163/163 [==============================] - 115s 703ms/step - loss: 0.0514 - accuracy: 0.9806 - val_loss: 0.3937 - val_accuracy: 0.9022\n", + "Epoch 16/25\n", + "163/163 [==============================] - 113s 692ms/step - loss: 0.0527 - accuracy: 0.9812 - val_loss: 0.2983 - val_accuracy: 0.9279\n", + "Epoch 17/25\n", + "163/163 [==============================] - 124s 758ms/step - loss: 0.0451 - accuracy: 0.9843 - val_loss: 0.2324 - val_accuracy: 0.9279\n", + "Epoch 18/25\n", + "163/163 [==============================] - 177s 1s/step - loss: 0.0487 - accuracy: 0.9837 - val_loss: 0.3608 - val_accuracy: 0.9087\n", + "Epoch 19/25\n", + "163/163 [==============================] - 199s 1s/step - loss: 0.0475 - accuracy: 0.9841 - val_loss: 0.2932 - val_accuracy: 0.9038\n", + "Epoch 20/25\n", + "163/163 [==============================] - 199s 1s/step - loss: 0.0559 - accuracy: 0.9793 - val_loss: 0.2245 - val_accuracy: 0.9183\n", + "Epoch 21/25\n", + "163/163 [==============================] - 195s 1s/step - loss: 0.0458 - accuracy: 0.9843 - val_loss: 0.3297 - val_accuracy: 0.9215\n", + "Epoch 22/25\n", + "163/163 [==============================] - 192s 1s/step - loss: 0.0539 - accuracy: 0.9806 - val_loss: 0.3120 - val_accuracy: 0.9071\n", + "Epoch 23/25\n", + "163/163 [==============================] - 200s 1s/step - loss: 0.0417 - accuracy: 0.9833 - val_loss: 0.6652 - val_accuracy: 0.8574\n", + "Epoch 24/25\n", + "163/163 [==============================] - 199s 1s/step - loss: 0.0503 - accuracy: 0.9822 - val_loss: 0.4144 - val_accuracy: 0.8926\n", + "Epoch 25/25\n", + "163/163 [==============================] - 201s 1s/step - loss: 0.0442 - accuracy: 0.9850 - val_loss: 0.4151 - val_accuracy: 0.8958\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<tensorflow.python.keras.callbacks.History at 0x226801ea688>" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 37 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AiCsHRNnwLYw", + "colab_type": "text" + }, + "source": [ + "### Making a prediction" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "N5SdP_4dwLYy", + "colab_type": "code", + "colab": {}, + "outputId": "08cd96f7-2355-46b3-9f9c-375cc240637c" + }, + "source": [ + "test_image = image.load_img('test/PNEUMONIA/person1_virus_7.jpeg', target_size=(64, 64))\n", + "test_image" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": 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\n", + "text/plain": [ + "<PIL.Image.Image image mode=RGB size=64x64 at 0x22680735AC8>" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 38 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ojQqZNx1wLY8", + "colab_type": "code", + "colab": {}, + "outputId": "8dd2143c-ac51-49a8-80ae-72a16ecec5e8" + }, + "source": [ + "test_image = image.img_to_array(test_image)\n", + "test_image" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[[134., 134., 134.],\n", + " [ 45., 45., 45.],\n", + " [ 49., 49., 49.],\n", + " ...,\n", + " [222., 222., 222.],\n", + " [205., 205., 205.],\n", + " [213., 213., 213.]],\n", + "\n", + " [[ 53., 53., 53.],\n", + " [ 40., 40., 40.],\n", + " [ 60., 60., 60.],\n", + " ...,\n", + " [234., 234., 234.],\n", + " [209., 209., 209.],\n", + " [211., 211., 211.]],\n", + "\n", + " [[ 53., 53., 53.],\n", + " [ 48., 48., 48.],\n", + " [ 70., 70., 70.],\n", + " ...,\n", + " [185., 185., 185.],\n", + " [212., 212., 212.],\n", + " [216., 216., 216.]],\n", + "\n", + " ...,\n", + "\n", + " [[ 24., 24., 24.],\n", + " [ 26., 26., 26.],\n", + " [ 26., 26., 26.],\n", + " ...,\n", + " [ 17., 17., 17.],\n", + " [ 19., 19., 19.],\n", + " [ 18., 18., 18.]],\n", + "\n", + " [[ 26., 26., 26.],\n", + " [ 26., 26., 26.],\n", + " [ 26., 26., 26.],\n", + " ...,\n", + " [ 16., 16., 16.],\n", + " [ 19., 19., 19.],\n", + " [ 20., 20., 20.]],\n", + "\n", + " [[ 26., 26., 26.],\n", + " [ 26., 26., 26.],\n", + " [ 24., 24., 24.],\n", + " ...,\n", + " [ 16., 16., 16.],\n", + " [ 19., 19., 19.],\n", + " [ 19., 19., 19.]]], dtype=float32)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 39 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "7LjOjssdwLZJ", + "colab_type": "code", + "colab": {}, + "outputId": "869f1d47-d72f-4b60-f4c9-f9693da95a47" + }, + "source": [ + "test_image = np.expand_dims(test_image, axis=0)\n", + "test_image" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[[[134., 134., 134.],\n", + " [ 45., 45., 45.],\n", + " [ 49., 49., 49.],\n", + " ...,\n", + " [222., 222., 222.],\n", + " [205., 205., 205.],\n", + " [213., 213., 213.]],\n", + "\n", + " [[ 53., 53., 53.],\n", + " [ 40., 40., 40.],\n", + " [ 60., 60., 60.],\n", + " ...,\n", + " [234., 234., 234.],\n", + " [209., 209., 209.],\n", + " [211., 211., 211.]],\n", + "\n", + " [[ 53., 53., 53.],\n", + " [ 48., 48., 48.],\n", + " [ 70., 70., 70.],\n", + " ...,\n", + " [185., 185., 185.],\n", + " [212., 212., 212.],\n", + " [216., 216., 216.]],\n", + "\n", + " ...,\n", + "\n", + " [[ 24., 24., 24.],\n", + " [ 26., 26., 26.],\n", + " [ 26., 26., 26.],\n", + " ...,\n", + " [ 17., 17., 17.],\n", + " [ 19., 19., 19.],\n", + " [ 18., 18., 18.]],\n", + "\n", + " [[ 26., 26., 26.],\n", + " [ 26., 26., 26.],\n", + " [ 26., 26., 26.],\n", + " ...,\n", + " [ 16., 16., 16.],\n", + " [ 19., 19., 19.],\n", + " [ 20., 20., 20.]],\n", + "\n", + " [[ 26., 26., 26.],\n", + " [ 26., 26., 26.],\n", + " [ 24., 24., 24.],\n", + " ...,\n", + " [ 16., 16., 16.],\n", + " [ 19., 19., 19.],\n", + " [ 19., 19., 19.]]]], dtype=float32)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 40 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3chAjHsEwLZV", + "colab_type": "code", + "colab": {}, + "outputId": "f375eb94-3dca-47d3-de20-82ced809c582" + }, + "source": [ + "result = cnn.predict(test_image)\n", + "result" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[1.]], dtype=float32)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 41 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ew5t1Q2KwLZf", + "colab_type": "code", + "colab": {}, + "outputId": "9a393185-1313-4a92-cdb4-d97a25d307de" + }, + "source": [ + "training_set.class_indices" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'NORMAL': 0, 'PNEUMONIA': 1}" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 42 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0PRTcxumwLZt", + "colab_type": "code", + "colab": {}, + "outputId": "9c48017f-be59-4f78-bc99-feffaa49fcc3" + }, + "source": [ + "if result[0][0] == 1:\n", + " prediction = 'Yes'\n", + "else:\n", + " prediction = 'No'\n", + " \n", + "print(prediction)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Yes\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iTJzvNZswLaC", + "colab_type": "code", + "colab": {}, + "outputId": "b973828e-51a1-48f4-deb5-f7fd3acf70e4" + }, + "source": [ + "predictions = cnn.predict(test_set)\n", + "predictions" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + 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