brain_tumor_detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Preetirai-tech/Brain-Tumor-Detection/blob/main/brain_tumor_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "a3f1615a",
      "metadata": {
        "id": "a3f1615a"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import os\n",
        "import random\n",
        "import math, shutil\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.models import load_model\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense, BatchNormalization, GlobalAvgPool2D\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "b96aaee7",
      "metadata": {
        "id": "b96aaee7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce2b56e8-b208-4749-ec2f-1477d9021fa6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('no', 1500), ('yes', 1500)])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "DIR = '/content/drive/MyDrive/Brain tumor detection/datasets'\n",
        "\n",
        "num_of_images = {}\n",
        "\n",
        "for dir in os.listdir(DIR):\n",
        "  num_of_images[dir] = len(os.listdir(os.path.join(DIR, dir)))\n",
        "\n",
        "num_of_images.items()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splited the data such that\n",
        "  * 70% for Train data\n",
        "  * 15% for Validation\n",
        "  * 15% for Testing"
      ],
      "metadata": {
        "id": "wAnvDjvW_EIr"
      },
      "id": "wAnvDjvW_EIr"
    },
    {
      "cell_type": "code",
      "source": [
        "def create_data_folder(folder_name, split):\n",
        "  if not os.path.exists('./' + folder_name):\n",
        "    os.mkdir('./' + folder_name)\n",
        "\n",
        "    for dir in os.listdir(DIR):\n",
        "      os.makedirs('./' + folder_name + '/' + dir)\n",
        "\n",
        "      for img in np.random.choice(a = os.listdir(os.path.join(DIR, dir)), \n",
        "                                  size = (math.floor(split * num_of_images[dir])-2), \n",
        "                                  replace = False):\n",
        "        file_path = os.path.join(DIR, dir, img)\n",
        "        file = os.path.join('./' + folder_name, dir)\n",
        "        shutil.copy(file_path, file)\n",
        "        os.remove(file_path)\n",
        "\n",
        "  else:\n",
        "    print(f'{path} folder exists')\n",
        "\n"
      ],
      "metadata": {
        "id": "X2kZs0oxKqah"
      },
      "id": "X2kZs0oxKqah",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "create_data_folder('train', 0.7)"
      ],
      "metadata": {
        "id": "45XUpjmIT-ju"
      },
      "id": "45XUpjmIT-ju",
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "create_data_folder('val', 0.15)"
      ],
      "metadata": {
        "id": "BXJjYfvJVXIZ"
      },
      "id": "BXJjYfvJVXIZ",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "create_data_folder('test', 0.15)"
      ],
      "metadata": {
        "id": "uQYK7isuTd5N"
      },
      "id": "uQYK7isuTd5N",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_of_images = {}\n",
        "\n",
        "for dir in os.listdir(DIR):\n",
        "  num_of_images[dir] = len(os.listdir(os.path.join(DIR, dir)))\n",
        "\n",
        "num_of_images.items()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNp0Wf1X73Bf",
        "outputId": "76f9cc7e-ee5b-4f3e-998a-9a2464a14483"
      },
      "id": "sNp0Wf1X73Bf",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('no', 6), ('yes', 6)])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Preprocessing"
      ],
      "metadata": {
        "id": "1NAw6yAubgFV"
      },
      "id": "1NAw6yAubgFV"
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing for train data\n",
        "def preprocessingImages1(path):\n",
        "  \"\"\"\n",
        "  Input: Path\n",
        "  Output: Preprocessed images\n",
        "  \"\"\"\n",
        "  image_data = ImageDataGenerator(zoom_range = 0.2, shear_range = 0.2, rescale = 1/255, horizontal_flip = True)\n",
        "  image = image_data.flow_from_directory(directory = path, target_size = (224, 224), batch_size = 32, class_mode = 'binary')\n",
        "\n",
        "  return image"
      ],
      "metadata": {
        "id": "A_-ZtXLZcGcK"
      },
      "id": "A_-ZtXLZcGcK",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/train'\n",
        "train_data = preprocessingImages1(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3ff-RnneHgX",
        "outputId": "3c70ff3b-d4e8-4fd2-b633-5c328f3e063f"
      },
      "id": "M3ff-RnneHgX",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2096 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.batch_size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LTNNlwLTQ5UD",
        "outputId": "b808c6d4-c9a4-46c4-8982-2ad0dc1d258f"
      },
      "id": "LTNNlwLTQ5UD",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing for validation and test data\n",
        "def preprocessingImages2(path):\n",
        "  \"\"\"\n",
        "  Input: Path\n",
        "  Output: Preprocessed images\n",
        "  \"\"\"\n",
        "  image_data = ImageDataGenerator(rescale = 1/255)\n",
        "  image = image_data.flow_from_directory(directory = path, target_size = (224, 224), batch_size = 32, class_mode = 'binary')\n",
        "  \n",
        "  return image"
      ],
      "metadata": {
        "id": "X9W60dsvbqwC"
      },
      "id": "X9W60dsvbqwC",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/test'\n",
        "test_data = preprocessingImages2(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wx4DmfqQe5pw",
        "outputId": "a2c7a042-63a2-4aea-d728-7df044e9989f"
      },
      "id": "Wx4DmfqQe5pw",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 446 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/val'\n",
        "val_data = preprocessingImages2(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-S1Qr7_fPaQ",
        "outputId": "f09da9c0-f746-433f-afa6-a685aae2f9c7"
      },
      "id": "c-S1Qr7_fPaQ",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 446 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4969db36",
      "metadata": {
        "id": "4969db36"
      },
      "source": [
        "### Model Build"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "bcccbff1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcccbff1",
        "outputId": "fd935c45-63e0-444e-8422-3a5fdc82aead"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_16 (Conv2D)          (None, 222, 222, 16)      448       \n",
            "                                                                 \n",
            " conv2d_17 (Conv2D)          (None, 220, 220, 36)      5220      \n",
            "                                                                 \n",
            " max_pooling2d_12 (MaxPoolin  (None, 110, 110, 36)     0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_18 (Conv2D)          (None, 108, 108, 64)      20800     \n",
            "                                                                 \n",
            " max_pooling2d_13 (MaxPoolin  (None, 54, 54, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_19 (Conv2D)          (None, 52, 52, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_14 (MaxPoolin  (None, 26, 26, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 26, 26, 128)       0         \n",
            "                                                                 \n",
            " flatten_4 (Flatten)         (None, 86528)             0         \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 64)                5537856   \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,638,245\n",
            "Trainable params: 5,638,245\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# CNN Model\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters = 16, kernel_size= (3,3), activation= 'relu', input_shape = (224, 224, 3)))\n",
        "\n",
        "model.add(Conv2D(filters = 36, kernel_size= (3,3), activation= 'relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters = 64, kernel_size= (3,3), activation= 'relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters = 128, kernel_size= (3,3), activation= 'relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Dropout(rate = 0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units = 64, activation = 'relu'))\n",
        "model.add(Dropout(rate = 0.25))\n",
        "model.add(Dense(units = 1, activation= 'sigmoid'))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "cfc848a7",
      "metadata": {
        "id": "cfc848a7"
      },
      "outputs": [],
      "source": [
        "model.compile(loss = 'binary_crossentropy', optimizer='adam', metrics = ['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "1b96d0dd",
      "metadata": {
        "id": "1b96d0dd"
      },
      "outputs": [],
      "source": [
        "# Early stopping\n",
        "\n",
        "es = EarlyStopping(monitor='val_loss', \n",
        "                   #min_delta= 0.01,\n",
        "                   patience= 3,\n",
        "                   verbose= 1,\n",
        "                   mode = 'auto',\n",
        "                   baseline=None,\n",
        "                   restore_best_weights=False)\n",
        "\n",
        "# Model check point\n",
        "\n",
        "mc = ModelCheckpoint(monitor='val_loss',\n",
        "                   filepath= './bestmodel.h5',\n",
        "                   verbose=1,\n",
        "                   save_best_only= True,\n",
        "                   mode = 'auto')\n",
        "\n",
        "cb = [es, mc]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5e41d65f",
      "metadata": {
        "id": "5e41d65f"
      },
      "source": [
        "### Model Training"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "STEP_SIZE_TRAIN = train_data.n // train_data.batch_size\n",
        "STEP_SIZE_VALID = val_data.n // val_data.batch_size\n",
        "history = model.fit(train_data,\n",
        "                    steps_per_epoch = STEP_SIZE_TRAIN,\n",
        "                    epochs= 100, verbose= 1, \n",
        "                    validation_data= val_data,\n",
        "                    validation_steps = STEP_SIZE_VALID,\n",
        "                    callbacks= cb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsMlNDG-MrjU",
        "outputId": "0029a512-b1fd-485e-e0a8-6d82717429cc"
      },
      "id": "HsMlNDG-MrjU",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.6439 - accuracy: 0.6609\n",
            "Epoch 1: val_loss improved from inf to 0.51622, saving model to ./bestmodel.h5\n",
            "65/65 [==============================] - 31s 447ms/step - loss: 0.6439 - accuracy: 0.6609 - val_loss: 0.5162 - val_accuracy: 0.7332\n",
            "Epoch 2/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.5374 - accuracy: 0.7330\n",
            "Epoch 2: val_loss improved from 0.51622 to 0.42725, saving model to ./bestmodel.h5\n",
            "65/65 [==============================] - 30s 466ms/step - loss: 0.5374 - accuracy: 0.7330 - val_loss: 0.4272 - val_accuracy: 0.8365\n",
            "Epoch 3/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.4673 - accuracy: 0.7984\n",
            "Epoch 3: val_loss did not improve from 0.42725\n",
            "65/65 [==============================] - 30s 461ms/step - loss: 0.4673 - accuracy: 0.7984 - val_loss: 0.4325 - val_accuracy: 0.8125\n",
            "Epoch 4/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.4235 - accuracy: 0.8222\n",
            "Epoch 4: val_loss improved from 0.42725 to 0.36326, saving model to ./bestmodel.h5\n",
            "65/65 [==============================] - 29s 448ms/step - loss: 0.4235 - accuracy: 0.8222 - val_loss: 0.3633 - val_accuracy: 0.8510\n",
            "Epoch 5/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.3606 - accuracy: 0.8469\n",
            "Epoch 5: val_loss improved from 0.36326 to 0.28273, saving model to ./bestmodel.h5\n",
            "65/65 [==============================] - 29s 444ms/step - loss: 0.3606 - accuracy: 0.8469 - val_loss: 0.2827 - val_accuracy: 0.8966\n",
            "Epoch 6/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.3457 - accuracy: 0.8585\n",
            "Epoch 6: val_loss did not improve from 0.28273\n",
            "65/65 [==============================] - 30s 461ms/step - loss: 0.3457 - accuracy: 0.8585 - val_loss: 0.3426 - val_accuracy: 0.8798\n",
            "Epoch 7/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.3218 - accuracy: 0.8740\n",
            "Epoch 7: val_loss did not improve from 0.28273\n",
            "65/65 [==============================] - 30s 462ms/step - loss: 0.3218 - accuracy: 0.8740 - val_loss: 0.2927 - val_accuracy: 0.8894\n",
            "Epoch 8/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.2692 - accuracy: 0.8963\n",
            "Epoch 8: val_loss improved from 0.28273 to 0.26417, saving model to ./bestmodel.h5\n",
            "65/65 [==============================] - 30s 466ms/step - loss: 0.2692 - accuracy: 0.8963 - val_loss: 0.2642 - val_accuracy: 0.9135\n",
            "Epoch 9/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.2427 - accuracy: 0.9036\n",
            "Epoch 9: val_loss improved from 0.26417 to 0.19007, saving model to ./bestmodel.h5\n",
            "65/65 [==============================] - 31s 469ms/step - loss: 0.2427 - accuracy: 0.9036 - val_loss: 0.1901 - val_accuracy: 0.9231\n",
            "Epoch 10/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.2531 - accuracy: 0.8973\n",
            "Epoch 10: val_loss did not improve from 0.19007\n",
            "65/65 [==============================] - 30s 463ms/step - loss: 0.2531 - accuracy: 0.8973 - val_loss: 0.2092 - val_accuracy: 0.9327\n",
            "Epoch 11/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.2211 - accuracy: 0.9162\n",
            "Epoch 11: val_loss did not improve from 0.19007\n",
            "65/65 [==============================] - 30s 463ms/step - loss: 0.2211 - accuracy: 0.9162 - val_loss: 0.2231 - val_accuracy: 0.9399\n",
            "Epoch 12/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.1934 - accuracy: 0.9254\n",
            "Epoch 12: val_loss improved from 0.19007 to 0.16993, saving model to ./bestmodel.h5\n",
            "65/65 [==============================] - 30s 465ms/step - loss: 0.1934 - accuracy: 0.9254 - val_loss: 0.1699 - val_accuracy: 0.9447\n",
            "Epoch 13/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.1771 - accuracy: 0.9370\n",
            "Epoch 13: val_loss improved from 0.16993 to 0.16704, saving model to ./bestmodel.h5\n",
            "65/65 [==============================] - 30s 462ms/step - loss: 0.1771 - accuracy: 0.9370 - val_loss: 0.1670 - val_accuracy: 0.9567\n",
            "Epoch 14/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.1442 - accuracy: 0.9501\n",
            "Epoch 14: val_loss improved from 0.16704 to 0.13574, saving model to ./bestmodel.h5\n",
            "65/65 [==============================] - 32s 487ms/step - loss: 0.1442 - accuracy: 0.9501 - val_loss: 0.1357 - val_accuracy: 0.9519\n",
            "Epoch 15/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.1383 - accuracy: 0.9428\n",
            "Epoch 15: val_loss did not improve from 0.13574\n",
            "65/65 [==============================] - 30s 451ms/step - loss: 0.1383 - accuracy: 0.9428 - val_loss: 0.1415 - val_accuracy: 0.9495\n",
            "Epoch 16/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.1252 - accuracy: 0.9540\n",
            "Epoch 16: val_loss did not improve from 0.13574\n",
            "65/65 [==============================] - 30s 467ms/step - loss: 0.1252 - accuracy: 0.9540 - val_loss: 0.1499 - val_accuracy: 0.9591\n",
            "Epoch 17/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.1203 - accuracy: 0.9543\n",
            "Epoch 17: val_loss improved from 0.13574 to 0.10426, saving model to ./bestmodel.h5\n",
            "65/65 [==============================] - 31s 474ms/step - loss: 0.1203 - accuracy: 0.9543 - val_loss: 0.1043 - val_accuracy: 0.9591\n",
            "Epoch 18/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.1293 - accuracy: 0.9482\n",
            "Epoch 18: val_loss did not improve from 0.10426\n",
            "65/65 [==============================] - 30s 466ms/step - loss: 0.1293 - accuracy: 0.9482 - val_loss: 0.1109 - val_accuracy: 0.9615\n",
            "Epoch 19/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.0853 - accuracy: 0.9690\n",
            "Epoch 19: val_loss did not improve from 0.10426\n",
            "65/65 [==============================] - 30s 467ms/step - loss: 0.0853 - accuracy: 0.9690 - val_loss: 0.1223 - val_accuracy: 0.9663\n",
            "Epoch 20/100\n",
            "65/65 [==============================] - ETA: 0s - loss: 0.0911 - accuracy: 0.9690\n",
            "Epoch 20: val_loss did not improve from 0.10426\n",
            "65/65 [==============================] - 31s 467ms/step - loss: 0.0911 - accuracy: 0.9690 - val_loss: 0.1665 - val_accuracy: 0.9663\n",
            "Epoch 20: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "b1ee519a",
      "metadata": {
        "id": "b1ee519a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca92b493-4e96-4e70-871a-3350db31244b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "# Model Graphical Interpretation\n",
        "\n",
        "history.history.keys()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'], label = 'train')\n",
        "plt.plot(history.history['val_loss'], label = 'val')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "mmOFEZ9NhR92",
        "outputId": "abbdbeb2-49e5-4e91-e131-3b3febea67cc"
      },
      "id": "mmOFEZ9NhR92",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'], label = 'train')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "eTD5rg3PWSRe",
        "outputId": "28e1078c-f998-452d-d8cb-d3cc31288149"
      },
      "id": "eTD5rg3PWSRe",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model\n",
        "\n",
        "model = load_model('/content/bestmodel.h5')\n"
      ],
      "metadata": {
        "id": "p_L5WaKQhR7a"
      },
      "id": "p_L5WaKQhR7a",
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Accuracy"
      ],
      "metadata": {
        "id": "YhMizJ5Wr6rN"
      },
      "id": "YhMizJ5Wr6rN"
    },
    {
      "cell_type": "code",
      "source": [
        "#test_data[1]"
      ],
      "metadata": {
        "id": "k73v2pkpfWYv"
      },
      "id": "k73v2pkpfWYv",
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc = model.evaluate_generator(test_data)[1]\n",
        "\n",
        "print(f'The accuracy of the model is {acc * 100} %')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7zT7Eu6hR4R",
        "outputId": "0f4a4d1f-a389-4476-e0cd-d8a7c02bac80"
      },
      "id": "p7zT7Eu6hR4R",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the model is 97.30941653251648 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "sqhBozZ6YmPS"
      },
      "id": "sqhBozZ6YmPS",
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.class_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ai1cBp6PhNTv",
        "outputId": "3cc4aba1-9eb8-470e-b277-d5c1bc4a0cb4"
      },
      "id": "ai1cBp6PhNTv",
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'no': 0, 'yes': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_tumor(path):\n",
        "\n",
        "  img = tf.keras.preprocessing.image.load_img(path, target_size = (224, 224))\n",
        "  input_array = tf.keras.preprocessing.image.img_to_array(img)/255\n",
        "\n",
        "\n",
        "  plt.imshow(input_array)\n",
        "  plt.show()\n",
        "\n",
        "  #print(input_array.shape)\n",
        "\n",
        "  input_array = np.expand_dims(input_array, axis = 0)\n",
        "\n",
        "  pred = model.predict(input_array)#[0][0]\n",
        "  #print(pred)\n",
        "  print()\n",
        "  if pred >= 0.5:\n",
        "    print('The MRI image is of Brain Tumor')\n",
        "\n",
        "  else:\n",
        "    print('The MRI is of Healthy brain')"
      ],
      "metadata": {
        "id": "N4kOpxMOhRv_"
      },
      "id": "N4kOpxMOhRv_",
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/Brain tumor detection/pred/pred0.jpg'\n",
        "predict_tumor(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "KsejumlnhRs-",
        "outputId": "48f80a2e-308a-439f-d0d3-9dabd4386eb9"
      },
      "id": "KsejumlnhRs-",
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 20ms/step\n",
            "\n",
            "The MRI is of Healthy brain\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/Brain tumor detection/pred/pred2.jpg'\n",
        "predict_tumor(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "3FWyj9chhRnv",
        "outputId": "ef478c34-461a-4fad-a43a-5804d599e013"
      },
      "id": "3FWyj9chhRnv",
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n",
            "\n",
            "The MRI image is of Brain Tumor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/Brain tumor detection/pred/pred10.jpg'\n",
        "predict_tumor(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "N1uayn7RtrXr",
        "outputId": "297bcf16-c9f1-4946-a305-c567ad683b4e"
      },
      "id": "N1uayn7RtrXr",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n",
            "\n",
            "The MRI image is of Brain Tumor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JiqheWy-trNr"
      },
      "id": "JiqheWy-trNr",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BCT7pAewtrD3"
      },
      "id": "BCT7pAewtrD3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k3MASTXgtq8M"
      },
      "id": "k3MASTXgtq8M",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yu9f3S84tq4P"
      },
      "id": "yu9f3S84tq4P",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3XTAH7OYtqyS"
      },
      "id": "3XTAH7OYtqyS",
      "execution_count": null,
      "outputs": []
    }
  ],
  "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"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}