109. Brain_Tumor_classification.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghVcamAV_POp",
        "outputId": "b281f75e-1bd8-43cb-d7c0-53e577d6dd66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting segmentation-models\n",
            "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
            "Collecting image-classifiers==1.0.0\n",
            "  Downloading image_classifiers-1.0.0-py3-none-any.whl (19 kB)\n",
            "Collecting efficientnet==1.0.0\n",
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            "Installing collected packages: keras-applications, image-classifiers, efficientnet, segmentation-models\n",
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          ]
        }
      ],
      "source": [
        "!pip install segmentation-models\n",
        "!pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --no-cache-dir gdown\n",
        "!gdown --id 1-Vm4Y4YTPSH_zM3xSTzB9RQvJqiVFwaw"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rcD6Ccuo_dbr",
        "outputId": "c3623528-2480-4488-991d-634a4f6e41a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.8/dist-packages (4.4.0)\n",
            "Collecting gdown\n",
            "  Downloading gdown-4.6.0-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.8/dist-packages (from gdown) (4.6.3)\n",
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            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Installing collected packages: gdown\n",
            "  Attempting uninstall: gdown\n",
            "    Found existing installation: gdown 4.4.0\n",
            "    Uninstalling gdown-4.4.0:\n",
            "      Successfully uninstalled gdown-4.4.0\n",
            "Successfully installed gdown-4.6.0\n",
            "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-Vm4Y4YTPSH_zM3xSTzB9RQvJqiVFwaw\n",
            "To: /content/Tumor.zip\n",
            "100% 156M/156M [00:00<00:00, 167MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install patool\n",
        "import patoolib\n",
        "patoolib.extract_archive('Tumor.zip')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "kwwa7MH0_hup",
        "outputId": "be8624e6-6c8c-4839-90a4-cf399b4902e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting patool\n",
            "  Downloading patool-1.12-py2.py3-none-any.whl (77 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.5/77.5 KB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: patool\n",
            "Successfully installed patool-1.12\n",
            "patool: Extracting Tumor.zip ...\n",
            "patool: running /usr/bin/7z x -o./Unpack_fcfmq4tg -- Tumor.zip\n",
            "patool: ... Tumor.zip extracted to `Tumor'.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Tumor'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import cv2\n",
        "\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "import os\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "E7r6vcfu_o7p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SIZE = 128\n",
        "\n",
        "train_images = []\n",
        "train_labels = [] \n",
        "\n",
        "for directory_path in glob.glob(\"/content/Tumor/Training/*\"):\n",
        "    label = directory_path.split(\"/\")[-1]\n",
        "    print(label)\n",
        "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
        "        #print(img_path)\n",
        "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)       \n",
        "        img = cv2.resize(img, (SIZE, SIZE))\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
        "        train_images.append(img)\n",
        "        train_labels.append(label)\n",
        "print(len(train_images))        \n",
        "train_images = np.array(train_images)\n",
        "train_labels = np.array(train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WqtNR35N_qmp",
        "outputId": "f7463784-38af-425a-94c0-b8c517066507"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pituitary\n",
            "glioma\n",
            "notumor\n",
            "meningioma\n",
            "5712\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_images = []\n",
        "test_labels = [] \n",
        "for directory_path in glob.glob(\"/content/Tumor/Testing/*\"):\n",
        "    fruit_label = directory_path.split(\"/\")[-1]\n",
        "    print(fruit_label)\n",
        "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
        "        #print(img_path)\n",
        "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
        "        img = cv2.resize(img, (SIZE, SIZE))\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
        "        test_images.append(img)\n",
        "        test_labels.append(fruit_label)\n",
        "print(len(test_images))            \n",
        "test_images = np.array(test_images)\n",
        "test_labels = np.array(test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hPHR9YA_sVF",
        "outputId": "d8684c0d-81a2-4a5c-b952-054d9ff6c093"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pituitary\n",
            "glioma\n",
            "notumor\n",
            "meningioma\n",
            "1311\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(test_labels)\n",
        "test_labels_encoded = le.transform(test_labels)\n",
        "le.fit(train_labels)\n",
        "train_labels_encoded = le.transform(train_labels)\n",
        "x_train, y_train, x_test, y_test = train_images, train_labels_encoded, test_images, test_labels_encoded\n",
        "\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "metadata": {
        "id": "43hTbdDHBK1N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "y_train_one_hot = to_categorical(y_train)\n",
        "y_test_one_hot = to_categorical(y_test)"
      ],
      "metadata": {
        "id": "XZbDSULPBLxJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "activation = 'sigmoid'\n",
        "\n",
        "feature_extractor = Sequential()\n",
        "feature_extractor.add(Conv2D(32, 3, activation = activation, padding = 'same', input_shape = (SIZE, SIZE, 3)))\n",
        "feature_extractor.add(BatchNormalization())\n",
        "\n",
        "feature_extractor.add(Conv2D(32, 3, activation = activation, padding = 'same', kernel_initializer = 'he_uniform'))\n",
        "feature_extractor.add(BatchNormalization())\n",
        "feature_extractor.add(MaxPooling2D())\n",
        "\n",
        "feature_extractor.add(Conv2D(64, 3, activation = activation, padding = 'same', kernel_initializer = 'he_uniform'))\n",
        "feature_extractor.add(BatchNormalization())\n",
        "\n",
        "feature_extractor.add(Conv2D(64, 3, activation = activation, padding = 'same', kernel_initializer = 'he_uniform'))\n",
        "feature_extractor.add(BatchNormalization())\n",
        "feature_extractor.add(MaxPooling2D())\n",
        "\n",
        "feature_extractor.add(Flatten())\n",
        "     "
      ],
      "metadata": {
        "id": "5UziBFUFBOOD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_no = 4\n",
        "x = feature_extractor.output  \n",
        "x = Dense(128, activation = activation, kernel_initializer = 'he_uniform')(x)\n",
        "prediction_layer = Dense(class_no, activation = 'softmax')(x)\n",
        "cnn_model = Model(inputs=feature_extractor.input, outputs=prediction_layer)\n",
        "cnn_model.compile(optimizer='rmsprop',loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
        "print(cnn_model.summary()) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B0gfFSTKBVsi",
        "outputId": "87628ade-3488-4d7a-c619-fd776a41f943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_input (InputLayer)   [(None, 128, 128, 3)]     0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 128, 128, 32)      896       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 128, 128, 32)     128       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 128, 128, 32)      9248      \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 128, 128, 32)     128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 64, 64, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 64, 64, 64)        18496     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 64, 64, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 64, 64, 64)        36928     \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 64, 64, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 32, 32, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 65536)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               8388736   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4)                 516       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,455,588\n",
            "Trainable params: 8,455,204\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = cnn_model.fit(x_train, y_train_one_hot, epochs=100, validation_data = (x_test, y_test_one_hot))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h0c0_udyB5lL",
        "outputId": "56730999-4bfb-475e-90fc-9082d4e66b36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0699 - accuracy: 0.9746 - val_loss: 0.6527 - val_accuracy: 0.8162\n",
            "Epoch 2/100\n",
            "179/179 [==============================] - 9s 50ms/step - loss: 0.0731 - accuracy: 0.9779 - val_loss: 0.3564 - val_accuracy: 0.8818\n",
            "Epoch 3/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0646 - accuracy: 0.9778 - val_loss: 0.2427 - val_accuracy: 0.9230\n",
            "Epoch 4/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0566 - accuracy: 0.9832 - val_loss: 0.2713 - val_accuracy: 0.9138\n",
            "Epoch 5/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0712 - accuracy: 0.9778 - val_loss: 0.6882 - val_accuracy: 0.7986\n",
            "Epoch 6/100\n",
            "179/179 [==============================] - 10s 56ms/step - loss: 0.0531 - accuracy: 0.9818 - val_loss: 0.2327 - val_accuracy: 0.9336\n",
            "Epoch 7/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0445 - accuracy: 0.9848 - val_loss: 0.2712 - val_accuracy: 0.9130\n",
            "Epoch 8/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0441 - accuracy: 0.9860 - val_loss: 0.2348 - val_accuracy: 0.9367\n",
            "Epoch 9/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0359 - accuracy: 0.9888 - val_loss: 0.2452 - val_accuracy: 0.9222\n",
            "Epoch 10/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0377 - accuracy: 0.9888 - val_loss: 0.4344 - val_accuracy: 0.8856\n",
            "Epoch 11/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0297 - accuracy: 0.9905 - val_loss: 0.2259 - val_accuracy: 0.9359\n",
            "Epoch 12/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0440 - accuracy: 0.9837 - val_loss: 0.2179 - val_accuracy: 0.9321\n",
            "Epoch 13/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0379 - accuracy: 0.9881 - val_loss: 0.3412 - val_accuracy: 0.9184\n",
            "Epoch 14/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0306 - accuracy: 0.9900 - val_loss: 0.2893 - val_accuracy: 0.9298\n",
            "Epoch 15/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0367 - accuracy: 0.9881 - val_loss: 0.2895 - val_accuracy: 0.9260\n",
            "Epoch 16/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0276 - accuracy: 0.9898 - val_loss: 0.2098 - val_accuracy: 0.9420\n",
            "Epoch 17/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0286 - accuracy: 0.9904 - val_loss: 0.2147 - val_accuracy: 0.9443\n",
            "Epoch 18/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0171 - accuracy: 0.9949 - val_loss: 0.3295 - val_accuracy: 0.9153\n",
            "Epoch 19/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0137 - accuracy: 0.9963 - val_loss: 0.2523 - val_accuracy: 0.9382\n",
            "Epoch 20/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0128 - accuracy: 0.9960 - val_loss: 0.7184 - val_accuracy: 0.8490\n",
            "Epoch 21/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0153 - accuracy: 0.9946 - val_loss: 0.2811 - val_accuracy: 0.9184\n",
            "Epoch 22/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0116 - accuracy: 0.9963 - val_loss: 0.3885 - val_accuracy: 0.9184\n",
            "Epoch 23/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0140 - accuracy: 0.9958 - val_loss: 0.2777 - val_accuracy: 0.9184\n",
            "Epoch 24/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0139 - accuracy: 0.9963 - val_loss: 0.3061 - val_accuracy: 0.9397\n",
            "Epoch 25/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0075 - accuracy: 0.9977 - val_loss: 0.2670 - val_accuracy: 0.9344\n",
            "Epoch 26/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0062 - accuracy: 0.9986 - val_loss: 0.2383 - val_accuracy: 0.9436\n",
            "Epoch 27/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0060 - accuracy: 0.9975 - val_loss: 0.2045 - val_accuracy: 0.9474\n",
            "Epoch 28/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0030 - accuracy: 0.9996 - val_loss: 0.2054 - val_accuracy: 0.9542\n",
            "Epoch 29/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0047 - accuracy: 0.9984 - val_loss: 0.2358 - val_accuracy: 0.9436\n",
            "Epoch 30/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.2298 - val_accuracy: 0.9512\n",
            "Epoch 31/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0013 - accuracy: 0.9998 - val_loss: 0.3407 - val_accuracy: 0.9359\n",
            "Epoch 32/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0033 - accuracy: 0.9991 - val_loss: 0.3241 - val_accuracy: 0.9375\n",
            "Epoch 33/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0032 - accuracy: 0.9988 - val_loss: 0.2831 - val_accuracy: 0.9466\n",
            "Epoch 34/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.3053 - val_accuracy: 0.9428\n",
            "Epoch 35/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.3202 - val_accuracy: 0.9336\n",
            "Epoch 36/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 8.3322e-04 - accuracy: 0.9998 - val_loss: 0.2704 - val_accuracy: 0.9428\n",
            "Epoch 37/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 7.6665e-04 - accuracy: 0.9998 - val_loss: 0.2356 - val_accuracy: 0.9527\n",
            "Epoch 38/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 0.0030 - accuracy: 0.9989 - val_loss: 0.2599 - val_accuracy: 0.9413\n",
            "Epoch 39/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 0.0031 - accuracy: 0.9989 - val_loss: 0.2848 - val_accuracy: 0.9382\n",
            "Epoch 40/100\n",
            "179/179 [==============================] - 10s 55ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.2571 - val_accuracy: 0.9497\n",
            "Epoch 41/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 2.3978e-04 - accuracy: 1.0000 - val_loss: 0.4128 - val_accuracy: 0.9352\n",
            "Epoch 42/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 8.8780e-04 - accuracy: 0.9998 - val_loss: 0.2624 - val_accuracy: 0.9497\n",
            "Epoch 43/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 9.2856e-05 - accuracy: 1.0000 - val_loss: 0.7925 - val_accuracy: 0.8993\n",
            "Epoch 44/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 1.1811e-04 - accuracy: 1.0000 - val_loss: 0.3320 - val_accuracy: 0.9565\n",
            "Epoch 45/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 5.3397e-05 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9542\n",
            "Epoch 46/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 4.0791e-05 - accuracy: 1.0000 - val_loss: 0.2499 - val_accuracy: 0.9527\n",
            "Epoch 47/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 2.3446e-05 - accuracy: 1.0000 - val_loss: 0.3118 - val_accuracy: 0.9497\n",
            "Epoch 48/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 3.2972e-05 - accuracy: 1.0000 - val_loss: 0.2885 - val_accuracy: 0.9550\n",
            "Epoch 49/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 1.2396e-05 - accuracy: 1.0000 - val_loss: 0.5125 - val_accuracy: 0.9352\n",
            "Epoch 50/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 3.6222e-05 - accuracy: 1.0000 - val_loss: 0.4144 - val_accuracy: 0.9474\n",
            "Epoch 51/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 3.9595e-05 - accuracy: 1.0000 - val_loss: 0.3502 - val_accuracy: 0.9527\n",
            "Epoch 52/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 6.3531e-06 - accuracy: 1.0000 - val_loss: 0.4273 - val_accuracy: 0.9481\n",
            "Epoch 53/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 6.2414e-06 - accuracy: 1.0000 - val_loss: 0.3451 - val_accuracy: 0.9527\n",
            "Epoch 54/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 9.6915e-06 - accuracy: 1.0000 - val_loss: 0.3326 - val_accuracy: 0.9512\n",
            "Epoch 55/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 1.5312e-06 - accuracy: 1.0000 - val_loss: 0.4631 - val_accuracy: 0.9466\n",
            "Epoch 56/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 4.0744e-05 - accuracy: 1.0000 - val_loss: 0.3655 - val_accuracy: 0.9527\n",
            "Epoch 57/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 5.2006e-06 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9542\n",
            "Epoch 58/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 9.3903e-07 - accuracy: 1.0000 - val_loss: 0.3619 - val_accuracy: 0.9489\n",
            "Epoch 59/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 1.9501e-06 - accuracy: 1.0000 - val_loss: 0.4744 - val_accuracy: 0.9428\n",
            "Epoch 60/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 2.0704e-06 - accuracy: 1.0000 - val_loss: 0.5773 - val_accuracy: 0.9336\n",
            "Epoch 61/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 9.2889e-06 - accuracy: 1.0000 - val_loss: 0.4112 - val_accuracy: 0.9504\n",
            "Epoch 62/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 1.2129e-05 - accuracy: 1.0000 - val_loss: 0.3683 - val_accuracy: 0.9580\n",
            "Epoch 63/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 5.3992e-07 - accuracy: 1.0000 - val_loss: 0.6481 - val_accuracy: 0.9474\n",
            "Epoch 64/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 1.2021e-06 - accuracy: 1.0000 - val_loss: 0.3828 - val_accuracy: 0.9527\n",
            "Epoch 65/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 8.5229e-07 - accuracy: 1.0000 - val_loss: 0.4927 - val_accuracy: 0.9504\n",
            "Epoch 66/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 2.5593e-06 - accuracy: 1.0000 - val_loss: 0.5057 - val_accuracy: 0.9428\n",
            "Epoch 67/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 7.0954e-07 - accuracy: 1.0000 - val_loss: 0.3709 - val_accuracy: 0.9519\n",
            "Epoch 68/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 7.3848e-07 - accuracy: 1.0000 - val_loss: 0.4081 - val_accuracy: 0.9527\n",
            "Epoch 69/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 1.3434e-06 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9558\n",
            "Epoch 70/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 3.9677e-07 - accuracy: 1.0000 - val_loss: 0.4496 - val_accuracy: 0.9550\n",
            "Epoch 71/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 1.7430e-07 - accuracy: 1.0000 - val_loss: 0.6211 - val_accuracy: 0.9481\n",
            "Epoch 72/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 9.6209e-08 - accuracy: 1.0000 - val_loss: 0.3346 - val_accuracy: 0.9565\n",
            "Epoch 73/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 3.7253e-08 - accuracy: 1.0000 - val_loss: 0.5160 - val_accuracy: 0.9504\n",
            "Epoch 74/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 1.1916e-07 - accuracy: 1.0000 - val_loss: 0.4218 - val_accuracy: 0.9542\n",
            "Epoch 75/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 4.9712e-08 - accuracy: 1.0000 - val_loss: 0.3950 - val_accuracy: 0.9573\n",
            "Epoch 76/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 4.4202e-08 - accuracy: 1.0000 - val_loss: 0.4367 - val_accuracy: 0.9558\n",
            "Epoch 77/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 5.0359e-08 - accuracy: 1.0000 - val_loss: 0.4562 - val_accuracy: 0.9519\n",
            "Epoch 78/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 1.7331e-07 - accuracy: 1.0000 - val_loss: 0.5567 - val_accuracy: 0.9405\n",
            "Epoch 79/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 1.1605e-07 - accuracy: 1.0000 - val_loss: 0.3590 - val_accuracy: 0.9588\n",
            "Epoch 80/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 4.2387e-08 - accuracy: 1.0000 - val_loss: 0.3660 - val_accuracy: 0.9558\n",
            "Epoch 81/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 2.0786e-08 - accuracy: 1.0000 - val_loss: 0.3749 - val_accuracy: 0.9588\n",
            "Epoch 82/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 1.4859e-08 - accuracy: 1.0000 - val_loss: 0.4881 - val_accuracy: 0.9542\n",
            "Epoch 83/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 1.2230e-08 - accuracy: 1.0000 - val_loss: 0.4729 - val_accuracy: 0.9558\n",
            "Epoch 84/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 1.2647e-08 - accuracy: 1.0000 - val_loss: 0.5060 - val_accuracy: 0.9542\n",
            "Epoch 85/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 1.3399e-08 - accuracy: 1.0000 - val_loss: 0.4531 - val_accuracy: 0.9550\n",
            "Epoch 86/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 9.2245e-09 - accuracy: 1.0000 - val_loss: 0.4230 - val_accuracy: 0.9535\n",
            "Epoch 87/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 9.4332e-09 - accuracy: 1.0000 - val_loss: 0.5094 - val_accuracy: 0.9542\n",
            "Epoch 88/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 9.6419e-09 - accuracy: 1.0000 - val_loss: 0.4298 - val_accuracy: 0.9558\n",
            "Epoch 89/100\n",
            "179/179 [==============================] - 10s 55ms/step - loss: 8.2019e-09 - accuracy: 1.0000 - val_loss: 0.4273 - val_accuracy: 0.9588\n",
            "Epoch 90/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 7.5967e-09 - accuracy: 1.0000 - val_loss: 0.4871 - val_accuracy: 0.9535\n",
            "Epoch 91/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 8.3062e-09 - accuracy: 1.0000 - val_loss: 0.4252 - val_accuracy: 0.9542\n",
            "Epoch 92/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 7.6175e-09 - accuracy: 1.0000 - val_loss: 0.5146 - val_accuracy: 0.9542\n",
            "Epoch 93/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 5.4888e-09 - accuracy: 1.0000 - val_loss: 0.4463 - val_accuracy: 0.9565\n",
            "Epoch 94/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 5.6975e-09 - accuracy: 1.0000 - val_loss: 0.4032 - val_accuracy: 0.9565\n",
            "Epoch 95/100\n",
            "179/179 [==============================] - 9s 53ms/step - loss: 5.8019e-09 - accuracy: 1.0000 - val_loss: 0.4526 - val_accuracy: 0.9580\n",
            "Epoch 96/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 5.4053e-09 - accuracy: 1.0000 - val_loss: 0.4513 - val_accuracy: 0.9573\n",
            "Epoch 97/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 7.0540e-09 - accuracy: 1.0000 - val_loss: 0.4914 - val_accuracy: 0.9535\n",
            "Epoch 98/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 5.1340e-09 - accuracy: 1.0000 - val_loss: 0.4332 - val_accuracy: 0.9558\n",
            "Epoch 99/100\n",
            "179/179 [==============================] - 9s 52ms/step - loss: 5.2801e-09 - accuracy: 1.0000 - val_loss: 0.4259 - val_accuracy: 0.9558\n",
            "Epoch 100/100\n",
            "179/179 [==============================] - 9s 51ms/step - loss: 5.3010e-09 - accuracy: 1.0000 - val_loss: 0.4282 - val_accuracy: 0.9558\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn_model.save(\"cnn_model.h5\")"
      ],
      "metadata": {
        "id": "-UQfpjRxCFm3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'y', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "gi0hOK4KCeS-",
        "outputId": "722b5837-0b65-4fa1-929e-0f843b1405c3"
      },
      "execution_count": null,
      "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": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "plt.plot(epochs, acc, 'y', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "VKJbsb2MCgcD",
        "outputId": "3c7c3578-f275-4b96-e459-9f2ced5a7342"
      },
      "execution_count": null,
      "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": [
        "prediction_NN = cnn_model.predict(x_test)\n",
        "prediction_NN = np.argmax(prediction_NN, axis=-1)\n",
        "prediction_NN = le.inverse_transform(prediction_NN)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HBI1huyCikO",
        "outputId": "1ba07204-098a-402c-ae5c-54fe15ef861d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41/41 [==============================] - 1s 15ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n=600  #Select the index of image to be loaded for testing\n",
        "img = x_test[n]\n",
        "plt.imshow(img)\n",
        "input_img = np.expand_dims(img, axis=0) #Expand dims so the input is (num images, x, y, c)\n",
        "prediction = np.argmax(cnn_model.predict(input_img))  #argmax to convert categorical back to original\n",
        "prediction = le.inverse_transform([prediction])  #Reverse the label encoder to original name\n",
        "print(\"The prediction for this image is: \", prediction)\n",
        "print(\"The actual label for this image is: \", test_labels[n])\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "FkQRSnoOClzL",
        "outputId": "b1e94b86-6027-4e2d-a835-ac9e2642db63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 24ms/step\n",
            "The prediction for this image is:  ['notumor']\n",
            "The actual label for this image is:  notumor\n"
          ]
        },
        {
          "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": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt   \n",
        "from sklearn import metrics\n",
        "import pandas as pd\n",
        "from sklearn.metrics import classification_report\n",
        "print (\"Accuracy = \", metrics.accuracy_score(test_labels, prediction_NN)) \n",
        "ax= plt.subplot()\n",
        "cm = confusion_matrix(test_labels, prediction_NN)\n",
        "#print(cm)\n",
        "sns.heatmap(cm, annot=True,cmap=\"Blues\",fmt='g')\n",
        "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "ax.set_title('Confusion Matrix'); \n",
        "ax.xaxis.set_ticklabels([\"Pituitary\", \"Glioma\", \"Notumor\",\"Meningioma\"]); \n",
        "ax.yaxis.set_ticklabels([\"Pituitary\", \"Glioma\", \"Notumor\",\"Meningioma\"]);\n",
        "d = {'Expected': test_labels, 'Predicted': prediction_NN}\n",
        "results = pd.DataFrame(data=d)\n",
        "print(classification_report(results['Expected'], results['Predicted']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "HGDOR_W0DPOi",
        "outputId": "b9b7c039-5c00-4073-87f0-2af7939fb355"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.9557589626239512\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma       0.96      0.91      0.93       300\n",
            "  meningioma       0.90      0.92      0.91       306\n",
            "     notumor       0.98      1.00      0.99       405\n",
            "   pituitary       0.98      0.98      0.98       300\n",
            "\n",
            "    accuracy                           0.96      1311\n",
            "   macro avg       0.95      0.95      0.95      1311\n",
            "weighted avg       0.96      0.96      0.96      1311\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X_for_RF = feature_extractor.predict(x_train) #This is out X input to RF\n",
        "\n",
        "#RANDOM FOREST\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "RF_model = RandomForestClassifier(n_estimators = 50, random_state = 42)\n",
        "# Train the model on training data\n",
        "RF_model.fit(X_for_RF, y_train) #For sklearn no one hot encoding\n",
        "\n",
        "#Send test data through same feature extractor process\n",
        "X_test_feature = feature_extractor.predict(x_test)\n",
        "#Now predict using the trained RF model. \n",
        "prediction_RF = RF_model.predict(X_test_feature)\n",
        "#Inverse le transform to get original label back. \n",
        "prediction_RF = le.inverse_transform(prediction_RF)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rz5yoQYQF7yG",
        "outputId": "2ef66a2c-9975-4983-b92b-5810eee98ebe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "179/179 [==============================] - 2s 12ms/step\n",
            "41/41 [==============================] - 1s 13ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print (\"Accuracy = \", metrics.accuracy_score(test_labels, prediction_RF)) \n",
        "ax= plt.subplot()\n",
        "cm = confusion_matrix(test_labels, prediction_RF)\n",
        "#print(cm)\n",
        "sns.heatmap(cm, annot=True,cmap=\"Blues\",fmt='g')\n",
        "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "ax.set_title('Confusion Matrix'); \n",
        "ax.xaxis.set_ticklabels([\"Pituitary\", \"Glioma\", \"Notumor\",\"Meningioma\"]); \n",
        "ax.yaxis.set_ticklabels([\"Pituitary\", \"Glioma\", \"Notumor\",\"Meningioma\"]);\n",
        "d = {'Expected': test_labels, 'Predicted': prediction_RF}\n",
        "results = pd.DataFrame(data=d)\n",
        "print(classification_report(results['Expected'], results['Predicted']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "_leTMSb7MasL",
        "outputId": "03af44a7-6f4f-4f4c-ccf1-f6bd0e6b6f1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.9244851258581236\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma       0.95      0.80      0.87       300\n",
            "  meningioma       0.83      0.89      0.86       306\n",
            "     notumor       0.98      1.00      0.99       405\n",
            "   pituitary       0.93      0.98      0.95       300\n",
            "\n",
            "    accuracy                           0.92      1311\n",
            "   macro avg       0.92      0.92      0.92      1311\n",
            "weighted avg       0.93      0.92      0.92      1311\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n=250 #Select the index of image to be loaded for testing\n",
        "img = x_test[n]\n",
        "plt.imshow(img)\n",
        "input_img = np.expand_dims(img, axis=0) #Expand dims so the input is (num images, x, y, c)\n",
        "input_img_features=feature_extractor.predict(input_img)\n",
        "prediction_RF = RF_model.predict(input_img_features)[0] \n",
        "prediction_RF = le.inverse_transform([prediction_RF])  #Reverse the label encoder to original name\n",
        "print(\"The prediction for this image is: \", prediction_RF)\n",
        "print(\"The actual label for this image is: \", test_labels[n])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "uO63C7kXMsrj",
        "outputId": "c5e4a20a-2d9a-486d-9be5-8d240f3c3697"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 23ms/step\n",
            "The prediction for this image is:  ['pituitary']\n",
            "The actual label for this image is:  pituitary\n"
          ]
        },
        {
          "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": [
        "import shutil\n",
        "shutil.copy(\"/content/yolov7_640_100.zip\",\"/content/drive/MyDrive/Colab Notebooks/Results\")"
      ],
      "metadata": {
        "id": "eAMRrFAFNVT-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fpn8ZnafNpdx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}