Covid-19 Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8f70e5c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications import Xception\n",
    "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0c6aa0df",
   "metadata": {},
   "outputs": [],
   "source": [
    "root_dir = \"COVID-19_Radiography_Dataset/\"\n",
    "classes = [\"COVID\", \"Normal\", \"Lung Opacity\", \"Viral Pneumonia\"]\n",
    "num_classes = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "920206e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = Xception(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "69733a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "predictions = Dense(num_classes, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=base_model.input, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "18acec94",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8ba24ba7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 31748 images belonging to 4 classes.\n",
      "Found 10582 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    validation_split=0.25,\n",
    "    horizontal_flip=True\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    root_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical',\n",
    "    subset=\"training\"\n",
    ")\n",
    "\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    root_dir,\n",
    "    target_size=(244, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical',\n",
    "    subset=\"validation\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b6b6ff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    epochs=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d607a1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"covid19.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea30f19e",
   "metadata": {},
   "outputs": [],
   "source": [
    "history_df = pd.DataFrame(history.history)\n",
    "history_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13f5ecf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loss, test_acc = model.evaluate(validation_generator)\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c1596df",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history[\"accuracy\"])\n",
    "plt.plot(history.history[\"val_accuracy\"])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend([\"train\", \"val\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e028d4fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history[\"loss\"])\n",
    "plt.plot(history.history[\"val_loss\"])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend([\"train\", \"val\"])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}