Brain Tumor Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "36e4945d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import AveragePooling2D\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "from imutils import paths\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import argparse\n",
    "import os\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8840aa5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['no', 'yes']\n",
      "253\n"
     ]
    }
   ],
   "source": [
    "# Load the images\n",
    "path = \"./Desktop/DataFlair/brain_tumor_dataset\"\n",
    "print(os.listdir(path))\n",
    "\n",
    "image_paths = list(paths.list_images(path))\n",
    "print(len(image_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "104d3139",
   "metadata": {},
   "outputs": [],
   "source": [
    "images = []\n",
    "labels = []\n",
    "\n",
    "for image_path in image_paths:\n",
    "    label = image_path.split(os.path.sep)[-2]\n",
    "    image = cv2.imread(image_path)\n",
    "    image = cv2.resize(image, (224, 224))\n",
    "\n",
    "    images.append(image)\n",
    "    labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c234585e",
   "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": [
    "# Plot an image\n",
    "def plot_image(image):\n",
    "    plt.imshow(image)\n",
    "\n",
    "plot_image(images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "516c6ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "images = np.array(images) / 255.0\n",
    "labels = np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "afe5f1ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 0.]\n"
     ]
    }
   ],
   "source": [
    "# Perform One-hot encoding\n",
    "label_binarizer = LabelBinarizer()\n",
    "labels = label_binarizer.fit_transform(labels)\n",
    "labels = to_categorical(labels)\n",
    "\n",
    "print(labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d66b37b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the dataset\n",
    "(train_X, test_X, train_Y, test_Y) = train_test_split(images, labels, test_size= 0.10, random_state= 42, stratify= labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24aaced0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the Image Data Generator\n",
    "train_generator = ImageDataGenerator(fill_mode= 'nearest', rotation_range= 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aff5958a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model\n",
    "base_model = VGG16(weights= 'imagenet', input_tensor= Input(shape = (224, 224, 3)), include_top= False)\n",
    "base_input = base_model.input\n",
    "base_output = base_model.output\n",
    "base_output = AveragePooling2D(pool_size=(4, 4))(base_output)\n",
    "base_output = Flatten(name=\"flatten\")(base_output)\n",
    "base_output = Dense(64, activation=\"relu\")(base_output)\n",
    "base_output = Dropout(0.5)(base_output)\n",
    "base_output = Dense(2, activation=\"softmax\")(base_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "546fee86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Freeze the layers\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5dce5e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 8\n",
    "train_steps = len(train_X) // batch_size\n",
    "validation_steps = len(test_X) // batch_size\n",
    "epochs = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a88b35c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs = base_input, outputs = base_output)\n",
    "\n",
    "model.compile(optimizer= Adam(learning_rate= 1e-3), metrics= ['accuracy'], loss= 'binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d6ebf137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "average_pooling2d (AveragePo (None, 1, 1, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                32832     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 130       \n",
      "=================================================================\n",
      "Total params: 14,747,650\n",
      "Trainable params: 32,962\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bbb1f481",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\envs\\tf_env\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "28/28 [==============================] - 41s 923ms/step - loss: 0.7442 - accuracy: 0.5645 - val_loss: 0.6099 - val_accuracy: 0.6538\n",
      "Epoch 2/25\n",
      "28/28 [==============================] - 5s 191ms/step - loss: 0.6245 - accuracy: 0.6375 - val_loss: 0.5811 - val_accuracy: 0.9231\n",
      "Epoch 3/25\n",
      "28/28 [==============================] - 5s 193ms/step - loss: 0.6124 - accuracy: 0.7072 - val_loss: 0.5235 - val_accuracy: 0.8846\n",
      "Epoch 4/25\n",
      "28/28 [==============================] - 5s 189ms/step - loss: 0.6030 - accuracy: 0.7344 - val_loss: 0.4911 - val_accuracy: 0.8462\n",
      "Epoch 5/25\n",
      "28/28 [==============================] - 5s 195ms/step - loss: 0.5833 - accuracy: 0.6977 - val_loss: 0.4667 - val_accuracy: 0.9231\n",
      "Epoch 6/25\n",
      "28/28 [==============================] - 5s 189ms/step - loss: 0.5562 - accuracy: 0.7483 - val_loss: 0.4377 - val_accuracy: 0.9231\n",
      "Epoch 7/25\n",
      "28/28 [==============================] - 5s 188ms/step - loss: 0.5593 - accuracy: 0.7531 - val_loss: 0.3982 - val_accuracy: 0.9231\n",
      "Epoch 8/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.4999 - accuracy: 0.7956 - val_loss: 0.4120 - val_accuracy: 0.9231\n",
      "Epoch 9/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.5369 - accuracy: 0.8012 - val_loss: 0.3763 - val_accuracy: 0.9615\n",
      "Epoch 10/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.4997 - accuracy: 0.7981 - val_loss: 0.3624 - val_accuracy: 0.9231\n",
      "Epoch 11/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.5064 - accuracy: 0.7862 - val_loss: 0.3551 - val_accuracy: 0.9231\n",
      "Epoch 12/25\n",
      "28/28 [==============================] - 6s 194ms/step - loss: 0.4631 - accuracy: 0.7852 - val_loss: 0.3355 - val_accuracy: 0.9231\n",
      "Epoch 13/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.4345 - accuracy: 0.8194 - val_loss: 0.2828 - val_accuracy: 0.9231\n",
      "Epoch 14/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.4303 - accuracy: 0.8058 - val_loss: 0.2673 - val_accuracy: 0.9231\n",
      "Epoch 15/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.4861 - accuracy: 0.7914 - val_loss: 0.2859 - val_accuracy: 0.9231\n",
      "Epoch 16/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.4944 - accuracy: 0.7779 - val_loss: 0.2906 - val_accuracy: 0.9231\n",
      "Epoch 17/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.4058 - accuracy: 0.8336 - val_loss: 0.2462 - val_accuracy: 0.9615\n",
      "Epoch 18/25\n",
      "28/28 [==============================] - 5s 189ms/step - loss: 0.4484 - accuracy: 0.8258 - val_loss: 0.2390 - val_accuracy: 0.9615\n",
      "Epoch 19/25\n",
      "28/28 [==============================] - 5s 193ms/step - loss: 0.4948 - accuracy: 0.8274 - val_loss: 0.3201 - val_accuracy: 0.9231\n",
      "Epoch 20/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.4312 - accuracy: 0.7862 - val_loss: 0.2174 - val_accuracy: 0.9231\n",
      "Epoch 21/25\n",
      "28/28 [==============================] - 5s 192ms/step - loss: 0.3744 - accuracy: 0.8197 - val_loss: 0.2502 - val_accuracy: 0.9231\n",
      "Epoch 22/25\n",
      "28/28 [==============================] - 5s 191ms/step - loss: 0.3812 - accuracy: 0.8842 - val_loss: 0.2650 - val_accuracy: 0.9231\n",
      "Epoch 23/25\n",
      "28/28 [==============================] - 5s 191ms/step - loss: 0.4223 - accuracy: 0.7911 - val_loss: 0.2199 - val_accuracy: 0.9615\n",
      "Epoch 24/25\n",
      "28/28 [==============================] - 5s 194ms/step - loss: 0.3806 - accuracy: 0.8648 - val_loss: 0.2853 - val_accuracy: 0.9231\n",
      "Epoch 25/25\n",
      "28/28 [==============================] - 5s 190ms/step - loss: 0.4313 - accuracy: 0.8495 - val_loss: 0.2380 - val_accuracy: 0.9231\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "history = model.fit_generator(train_generator.flow(train_X, train_Y, batch_size= batch_size),\n",
    "                              steps_per_epoch= train_steps,\n",
    "                              validation_data = (test_X, test_Y),\n",
    "                              validation_steps= validation_steps,\n",
    "                              epochs= epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d84d2165",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the model\n",
    "predictions = model.predict(test_X, batch_size= batch_size)\n",
    "predictions = np.argmax(predictions, axis= 1)\n",
    "actuals = np.argmax(test_Y, axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c1f282bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          no       0.83      1.00      0.91        10\n",
      "         yes       1.00      0.88      0.93        16\n",
      "\n",
      "    accuracy                           0.92        26\n",
      "   macro avg       0.92      0.94      0.92        26\n",
      "weighted avg       0.94      0.92      0.92        26\n",
      "\n",
      "[[10  0]\n",
      " [ 2 14]]\n"
     ]
    }
   ],
   "source": [
    "# Print Classification report and Confusion matrix\n",
    "print(classification_report(actuals, predictions, target_names= label_binarizer.classes_))\n",
    "\n",
    "cm = confusion_matrix(actuals, predictions)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0e1ddb7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9231\n"
     ]
    }
   ],
   "source": [
    "# Final accuracy of our model\n",
    "total = sum(sum(cm))\n",
    "accuracy = (cm[0, 0] + cm[1, 1]) / total\n",
    "print(\"Accuracy: {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "00a9551d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the losses and accuracies\n",
    "N = epochs\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "plt.plot(np.arange(0, N), history.history[\"loss\"], label= \"train_loss\")\n",
    "plt.plot(np.arange(0, N), history.history[\"val_loss\"], label= \"val_loss\")\n",
    "\n",
    "plt.plot(np.arange(0, N), history.history[\"accuracy\"], label= \"train_acc\")\n",
    "plt.plot(np.arange(0, N), history.history[\"val_accuracy\"], label= \"val_acc\")\n",
    "\n",
    "plt.title(\"Training Loss and Accuracy on Brain Dataset\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss / Accuracy\")\n",
    "plt.legend(loc= \"lower left\")\n",
    "plt.savefig(\"plot.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4234e93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f734acc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a988af9",
   "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}