finalyrproj.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "14jX8pnG9mqszB6rhqKc71gEm1k3Zw-Co",
      "authorship_tag": "ABX9TyPHTz4WCywLIWTq1BCgdLNJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/keshavg-45/AlzheimersDetetction/blob/main/finalyrproj.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKS_6GUvxwkB",
        "outputId": "e5577fd3-539f-4f34-9b40-fa6009a3a08a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLdlGVM7dKZh",
        "outputId": "29ff3922-1f3f-40d4-a844-848dd7323ac2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install classification-models-3D Keras-Applications -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ci8CiwBKctqq",
        "outputId": "7465dcff-483a-40aa-ad84-a183ad35b1c0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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    },
    {
      "cell_type": "code",
      "source": [
        "pip install wandb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ysUBgIOYcfkS",
        "outputId": "640e3815-a03b-4199-d782-9a603242ea88"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing Libraries"
      ],
      "metadata": {
        "id": "hzHby0u6cC1Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import random\n",
        "\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \n",
        "\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import nibabel as nib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sn\n",
        "import tensorflow as tf\n",
        "import wandb\n",
        "from classification_models_3D.tfkeras import Classifiers\n",
        "from ipywidgets import IntSlider, interact\n",
        "#from kaggle_secrets import UserSecretsClient\n",
        "from matplotlib import animation, rc\n",
        "from matplotlib.patches import PathPatch, Rectangle\n",
        "from matplotlib.path import Path\n",
        "from scipy import ndimage\n",
        "from scipy.ndimage import zoom\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "from tensorflow.keras import Input, Model\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
        "from tensorflow.keras.layers import (BatchNormalization, Conv3D, Dense,\n",
        "                                     Dropout, GlobalAveragePooling3D,\n",
        "                                     MaxPool3D)\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from wandb.keras import WandbCallback"
      ],
      "metadata": {
        "id": "S8GoqSAvcG3O"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import skimage.io\n",
        "import os \n",
        "import tqdm\n",
        "import glob\n",
        "import tensorflow \n",
        "\n",
        "from tqdm import tqdm\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from skimage.io import imread, imshow\n",
        "from skimage.transform import resize\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import InputLayer, BatchNormalization, Dropout, Flatten, Dense, Activation, MaxPool2D, Conv2D\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
        "from tensorflow.keras.applications.densenet import DenseNet201\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array"
      ],
      "metadata": {
        "id": "hOgs30UhrV9U"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Seed for Reproducibility"
      ],
      "metadata": {
        "id": "SXpyuHWvd6X8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed = 27\n",
        "\n",
        "random.seed(seed)\n",
        "os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "np.random.seed(seed)\n",
        "tf.random.set_seed(seed)\n",
        "tf.compat.v1.set_random_seed(seed)"
      ],
      "metadata": {
        "id": "gJeBLnb7d-vK"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Weights and Biases Setup"
      ],
      "metadata": {
        "id": "w8Y3R99ReB3w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Project Config"
      ],
      "metadata": {
        "id": "m1SUERrbkg0r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Loading and Processing"
      ],
      "metadata": {
        "id": "17Xf26Xrb6js"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def read_nifti_file(filepath):\n",
        "    \"\"\"Read and load volume\"\"\"\n",
        "    # Read file\n",
        "    scan = nib.load(filepath)\n",
        "    # Get raw data\n",
        "    scan = scan.get_fdata()\n",
        "    return scan\n",
        "\n",
        "\n",
        "def normalize(volume):\n",
        "    \"\"\"Normalize the volume\"\"\"\n",
        "    volume = (volume - np.min(volume)) / (np.max(volume) - np.min(volume))\n",
        "    return volume.astype('float32')\n",
        "\n",
        "\n",
        "def resize_volume(img, desired_width=64, desired_height=64, desired_depth=64):\n",
        "    \"\"\"Resize the volume\"\"\"\n",
        "    # Compute zoom factors\n",
        "    width_factor = desired_width / img.shape[0]\n",
        "    height_factor = desired_height / img.shape[1]\n",
        "    depth_factor = desired_depth / img.shape[-1]\n",
        "    # Rotate volume by 90 degrees\n",
        "    img = ndimage.rotate(img, 90, reshape=False)\n",
        "    # Resize the volume using spline interpolated zoom (SIZ)\n",
        "    img = zoom(img, (width_factor, height_factor, depth_factor), order=1)\n",
        "    return img\n",
        "\n",
        "\n",
        "def process_scan(path):\n",
        "    \"\"\"Read and resize volume\"\"\"\n",
        "    # Read scan\n",
        "    volume = read_nifti_file(path)\n",
        "    # Normalize\n",
        "    volume = normalize(volume)\n",
        "    # Resize width, height and depth\n",
        "#     volume = resize_volume(\n",
        "#         volume, config['img_size'], config['img_size'], config['depth']\n",
        "#     )\n",
        "    return volume"
      ],
      "metadata": {
        "id": "rB14BE6vb-0j"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class Distribution"
      ],
      "metadata": {
        "id": "xllrN_gBku6w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ad_paths = [\n",
        "#     os.path.join(os.getcwd(), '/kaggle/input/3cla-adni/AD', x)\n",
        "#     for x in os.listdir('/kaggle/input/3cla-adni/AD')\n",
        "# ]\n",
        "\n",
        "mci_paths = [\n",
        "    os.path.join(os.getcwd(), '/content/drive/MyDrive/dataset/MCI', x)\n",
        "    for x in os.listdir('/content/drive/MyDrive/dataset/MCI')\n",
        "]\n",
        "\n",
        "cn_paths = [\n",
        "    os.path.join(os.getcwd(), '/content/drive/MyDrive/dataset/CN', x)\n",
        "    for x in os.listdir('/content/drive/MyDrive/dataset/CN')\n",
        "]\n",
        "\n",
        "ad_paths = [\n",
        "    os.path.join(os.getcwd(), '/content/drive/MyDrive/dataset/AD', x)\n",
        "    for x in os.listdir('/content/drive/MyDrive/dataset/AD')\n",
        "]\n",
        "\n",
        "# print(f'Number of AD images: {len(ad_paths)}')\n",
        "print(f'Number of MCI images: {len(mci_paths)}')\n",
        "print(f'Number of NC images: {len(cn_paths)}')\n",
        "print(f'Number of AD images: {len(ad_paths)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMvsTX9kkwzQ",
        "outputId": "0f64498f-e125-432f-c4fe-0fd317aabacf"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of MCI images: 278\n",
            "Number of NC images: 346\n",
            "Number of AD images: 275\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build Train and Validation Datasets"
      ],
      "metadata": {
        "id": "rJErPIi1k8qk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mci_labels = np.array([0 for _ in range(len(mci_paths))])\n",
        "ad_labels = np.array([1 for _ in range(len(ad_paths))])\n",
        "nc_labels = np.array([1 for _ in range(len(cn_paths))])\n",
        "CLASS_NAMES = ['MCI', 'CN', 'AD']\n",
        "\n",
        "# labels = np.concatenate((np.concatenate((mci_labels, ad_labels), axis=0), nc_labels), axis=0)\n",
        "labels = np.concatenate((mci_labels, nc_labels, ad_labels), axis=0)\n",
        "# paths = np.concatenate((np.concatenate((mci_paths, ad_paths), axis=0), nc_paths), axis=0)\n",
        "paths = np.concatenate((mci_paths, cn_paths, ad_labels), axis=0)"
      ],
      "metadata": {
        "id": "YSR8KIwzk6x2"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_indices, test_indices = next(\n",
        "    StratifiedShuffleSplit(1, train_size=0.8, random_state=seed).split(\n",
        "        paths, labels\n",
        "    )\n",
        ")\n",
        "\n",
        "tmp_labels = [labels[idx] for idx in train_indices]\n",
        "tmp_paths = [paths[idx] for idx in train_indices]\n",
        "\n",
        "tmp_train_indices, tmp_val_indices = next(\n",
        "    StratifiedShuffleSplit(1, train_size=0.8, random_state=seed).split(\n",
        "        tmp_paths, tmp_labels\n",
        "    )\n",
        ")\n",
        "\n",
        "tmp_train_paths = [tmp_paths[idx] for idx in tmp_train_indices]\n",
        "tmp_val_paths = [tmp_paths[idx] for idx in tmp_val_indices]\n",
        "\n",
        "train_indices = [paths.tolist().index(path) for path in tmp_train_paths]\n",
        "val_indices = [paths.tolist().index(path) for path in tmp_val_paths]\n",
        "\n",
        "# Check if there are no common indices\n",
        "#assert bool(set(train_indices) & set(val_indices)) is False\n",
        "#assert bool(set(train_indices) & set(test_indices)) is False\n",
        "#assert bool(set(val_indices) & set(test_indices)) is False\n",
        "\n",
        "print(\n",
        "    'Number of samples:\\n'\n",
        "    f'train: {len(train_indices)}\\n'\n",
        "    f'validation: {len(val_indices)}\\n'\n",
        "    f'test: {len(test_indices)}'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EwPE3C7lD19",
        "outputId": "64b37fa1-25be-4647-d34d-17c04a768b82"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples:\n",
            "train: 575\n",
            "validation: 144\n",
            "test: 180\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Augmentation"
      ],
      "metadata": {
        "id": "LAnBrYs5lKd9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def random_rotate(volume):\n",
        "    \"\"\"Rotate the volume by a few degrees\"\"\"\n",
        "\n",
        "    def scipy_rotate(volume):\n",
        "        # define some rotation angles\n",
        "        angles = [-20, -10, -5, 5, 10, 20]\n",
        "        # pick angles at random\n",
        "        angle = random.choice(angles)\n",
        "        # rotate volume\n",
        "        volume = ndimage.rotate(volume, angle, reshape=False)\n",
        "        volume[volume < 0] = 0\n",
        "        volume[volume > 1] = 1\n",
        "        return volume\n",
        "\n",
        "    augmented_volume = tf.numpy_function(scipy_rotate, [volume], tf.float32)\n",
        "    return augmented_volume"
      ],
      "metadata": {
        "id": "45tCR9URlOwj"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class DataGenerator(tf.keras.utils.Sequence):\n",
        "    'Generates data for Keras'\n",
        "    def __init__(self, indices, paths, labels, batch_size=4, dim=(64, 64, 64),\n",
        "                 n_classes=2, shuffle=True, transform=None):\n",
        "        'Initialization'\n",
        "        self.dim = dim\n",
        "        self.batch_size = batch_size\n",
        "        self.paths = paths\n",
        "        self.labels = labels\n",
        "        self.indices = indices\n",
        "        self.n_classes = n_classes\n",
        "        self.shuffle = shuffle\n",
        "        self.transform = transform\n",
        "        self.on_epoch_end()\n",
        "\n",
        "    def __len__(self):\n",
        "        'Denotes the number of batches per epoch'\n",
        "        return int(np.floor(len(self.indices) / self.batch_size))\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        'Generate one batch of data'\n",
        "        # Generate indexes of the batch\n",
        "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
        "        # Find list of IDs\n",
        "        indices_temp = [self.indices[k] for k in indexes]\n",
        "\n",
        "        # Generate data\n",
        "        X, y = self.__data_generation(indices_temp)\n",
        "\n",
        "        return X, y\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        'Updates indexes after each epoch'\n",
        "        self.indexes = np.arange(len(self.indices))\n",
        "        if self.shuffle:\n",
        "            np.random.shuffle(self.indexes)\n",
        "\n",
        "    def __data_generation(self, indices_temp):\n",
        "        'Generates data containing batch_size samples'\n",
        "        # Initialization\n",
        "        X = np.empty((self.batch_size, *self.dim, 1))\n",
        "        y = np.empty((self.batch_size), dtype=int)\n",
        "\n",
        "        # Generate data\n",
        "        for i, ID in enumerate(indices_temp):\n",
        "            # Store sample\n",
        "            volume = process_scan(self.paths[ID])\n",
        "            if self.transform is not None:\n",
        "                volume = self.transform(volume)\n",
        "            X[i,] = np.expand_dims(volume, axis=3)\n",
        "\n",
        "            # Store class\n",
        "            y[i] = self.labels[ID]\n",
        "\n",
        "        return X, tf.keras.utils.to_categorical(y, num_classes=self.n_classes)"
      ],
      "metadata": {
        "id": "KGjIfzd4lTRV"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_generator = DataGenerator(\n",
        "    train_indices,\n",
        "    paths,\n",
        "    labels,\n",
        "    batch_size= 4,\n",
        "    shuffle=True,\n",
        "    transform=random_rotate,\n",
        ")\n",
        "\n",
        "validation_generator = DataGenerator(\n",
        "    val_indices, \n",
        "    paths,\n",
        "    labels, \n",
        "    batch_size= 4,\n",
        "    shuffle=True,\n",
        ")\n",
        "\n",
        "test_generator = DataGenerator(\n",
        "    test_indices,\n",
        "    paths,\n",
        "    labels, \n",
        "    batch_size= 1, \n",
        "    shuffle=False,\n",
        ")"
      ],
      "metadata": {
        "id": "9O4yARMAlW51"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Augmented MRI Visualization"
      ],
      "metadata": {
        "id": "iCS4ZmL9laBn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "images = process_scan(paths[0])\n",
        "images = np.expand_dims(images, axis=3)\n",
        "images = np.expand_dims(images, axis=0)\n",
        "image = images[0]\n",
        "\n",
        "print('Dimensions of the MRI scan are:', image.shape)\n",
        "plt.imshow(np.squeeze(image[:, :, 30]), cmap='bone')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "yJw0DcmglfAz",
        "outputId": "a2ae6814-23c2-4222-d549-cf79b5d11576"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dimensions of the MRI scan are: (113, 137, 113, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f109090f850>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "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": "markdown",
      "source": [
        "Slice Montage"
      ],
      "metadata": {
        "id": "ypVW7UPcllTG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_slices(num_rows, num_columns, width, height, data):\n",
        "    \"\"\"Plot a montage of CT slices\"\"\"\n",
        "    data = np.rot90(np.array(data))\n",
        "    data = np.transpose(data)\n",
        "    data = np.reshape(data, (num_rows, num_columns, width, height))\n",
        "    rows_data, columns_data = data.shape[0], data.shape[1]\n",
        "    heights = [slc[0].shape[0] for slc in data]\n",
        "    widths = [slc.shape[1] for slc in data[0]]\n",
        "    fig_width = 12.0\n",
        "    fig_height = fig_width * sum(heights) / sum(widths)\n",
        "    _, axarr = plt.subplots(\n",
        "        rows_data,\n",
        "        columns_data,\n",
        "        figsize=(fig_width, fig_height),\n",
        "        gridspec_kw={'height_ratios': heights},\n",
        "    )\n",
        "    for i in range(rows_data):\n",
        "        for j in range(columns_data):\n",
        "            axarr[i, j].imshow(data[i][j], cmap='bone')\n",
        "            axarr[i, j].axis('off')\n",
        "    plt.subplots_adjust(wspace=0, hspace=0, left=0, right=1, bottom=0, top=1)\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# Visualize montage of slices.\n",
        "# 4 rows and 10 columns for 40 slices of the CT scan.\n",
        "plot_slices(4, 10, 113, 137, image[:, :, :40])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "xIeesmlalj7u",
        "outputId": "4db84d1a-2b6b-4b54-ab31-6a65161c29fe"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x285.057 with 40 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Building"
      ],
      "metadata": {
        "id": "gG6dDvUFlrZE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "3D CNN"
      ],
      "metadata": {
        "id": "9KxIw4LMltdW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model(height=113, width=137, depth=113):\n",
        "    \"\"\"Build a 3D convolutional neural network model.\"\"\"\n",
        "    modelPoint, _ = Classifiers.get('resnet50')\n",
        "    net = modelPoint(\n",
        "        input_shape=(height, width, depth, 1),\n",
        "        include_top=False,\n",
        "        weights=None,\n",
        "    )\n",
        "    \n",
        "    x = net.layers[-1].output\n",
        "    x = GlobalAveragePooling3D()(x)    \n",
        "    x = Dense(units=128, activation='relu')(x)\n",
        "    x = Dense(units=64, activation='relu')(x)\n",
        "    outputs = Dense(units= 2, activation='softmax')(x)\n",
        "    \n",
        "    return Model(net.inputs, outputs, name='3D-ResNet50')\n",
        "\n",
        "\n",
        "# Build model.\n",
        "model = get_model(height=64, width=64, depth=64)\n",
        "# model.summary()"
      ],
      "metadata": {
        "id": "cCekYqJAlvfe"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training"
      ],
      "metadata": {
        "id": "uqgVfnTPl1IE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile model\n",
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer=Adam(learning_rate= 1e-5),\n",
        "    metrics=['acc'],\n",
        ")\n",
        "\n",
        "# Train the model, doing validation at the end of each epoch\n",
        "model.fit(\n",
        "    training_generator,\n",
        "    validation_data=validation_generator,\n",
        "    epochs=5,\n",
        "    verbose=2,\n",
        "    callbacks=[\n",
        "        ModelCheckpoint('best.h5', save_best_only=True, monitor='val_loss', mode='min'),\n",
        "        ReduceLROnPlateau(\n",
        "            monitor='val_loss',\n",
        "            mode='min',\n",
        "            patience=7,\n",
        "            factor=0.5,\n",
        "            min_lr= 1e-8,\n",
        "        ),\n",
        "        EarlyStopping(monitor='val_loss', mode='min', patience=30, restore_best_weights=True),\n",
        "    ],\n",
        "    workers=6\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-8TKTRJOl0jL",
        "outputId": "61897b97-8c39-43d4-b3b7-e87a8849b729"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "UnknownError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnknownError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-43-e64d5511dc47>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Train the model, doing validation at the end of each epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m model.fit(\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mtraining_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/wandb/integration/keras/keras.py\u001b[0m in \u001b[0;36mnew_v2\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mcbk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcbks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m                 \u001b[0mset_wandb_attrs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcbk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mold_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m     \u001b[0mtraining_arrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig_fit_loop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/wandb/integration/keras/keras.py\u001b[0m in \u001b[0;36mnew_v2\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mcbk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcbks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m                 \u001b[0mset_wandb_attrs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcbk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mold_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m     \u001b[0mtraining_arrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig_fit_loop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/wandb/integration/keras/keras.py\u001b[0m in \u001b[0;36mnew_v2\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mcbk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcbks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m                 \u001b[0mset_wandb_attrs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcbk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mold_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m     \u001b[0mtraining_arrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig_fit_loop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     53\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     54\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mUnknownError\u001b[0m: Graph execution error:\n\nFileNotFoundError: No such file or no access: '1'\nTraceback (most recent call last):\n\n  File \"/usr/local/lib/python3.8/dist-packages/nibabel/loadsave.py\", line 42, in load\n    stat_result = os.stat(filename)\n\nFileNotFoundError: [Errno 2] No such file or directory: '1'\n\n\nDuring handling of the above exception, another exception occurred:\n\n\nTraceback (most recent call last):\n\n  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/script_ops.py\", line 271, in __call__\n    ret = func(*args)\n\n  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py\", line 642, in wrapper\n    return func(*args, **kwargs)\n\n  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/dataset_ops.py\", line 1039, in generator_py_func\n    values = next(generator_state.get_iterator(iterator_id))\n\n  File \"/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py\", line 901, in wrapped_generator\n    for data in generator_fn():\n\n  File \"/usr/local/lib/python3.8/dist-packages/keras/utils/data_utils.py\", line 823, in get\n    raise e\n\n  File \"/usr/local/lib/python3.8/dist-packages/keras/utils/data_utils.py\", line 814, in get\n    inputs = self.queue.get(block=True, timeout=5).get()\n\n  File \"/usr/lib/python3.8/multiprocessing/pool.py\", line 771, in get\n    raise self._value\n\n  File \"/usr/lib/python3.8/multiprocessing/pool.py\", line 125, in worker\n    result = (True, func(*args, **kwds))\n\n  File \"/usr/local/lib/python3.8/dist-packages/keras/utils/data_utils.py\", line 600, in get_index\n    return _SHARED_SEQUENCES[uid][i]\n\n  File \"<ipython-input-34-81a41cff6029>\", line 28, in __getitem__\n    X, y = self.__data_generation(indices_temp)\n\n  File \"<ipython-input-34-81a41cff6029>\", line 47, in __data_generation\n    volume = process_scan(self.paths[ID])\n\n  File \"<ipython-input-27-041b8ed2c236>\", line 32, in process_scan\n    volume = read_nifti_file(path)\n\n  File \"<ipython-input-27-041b8ed2c236>\", line 4, in read_nifti_file\n    scan = nib.load(filepath)\n\n  File \"/usr/local/lib/python3.8/dist-packages/nibabel/loadsave.py\", line 44, in load\n    raise FileNotFoundError(\"No such file or no access: '%s'\" % filename)\n\nFileNotFoundError: No such file or no access: '1'\n\n\n\t [[{{node PyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_39075]"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing Training History"
      ],
      "metadata": {
        "id": "cuoL4GN9oT8J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 2, figsize=(20, 3))\n",
        "ax = ax.ravel()\n",
        "\n",
        "for i, metric in enumerate(['acc', 'loss']):\n",
        "    ax[i].plot(model.history.history[metric])\n",
        "    ax[i].plot(model.history.history[f'val_{metric}'])\n",
        "    ax[i].set_title(f'Model {metric}')\n",
        "    ax[i].set_xlabel('epochs')\n",
        "    ax[i].set_ylabel(metric)\n",
        "    ax[i].legend(['train', 'val'])"
      ],
      "metadata": {
        "id": "5H9-IULzoYaB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wandb.finish()"
      ],
      "metadata": {
        "id": "ka8JaHdeoeeL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction"
      ],
      "metadata": {
        "id": "P7dEQlK-ohns"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_weights('best.h5')\n",
        "input_volume = test_generator.__getitem__(1)[0]\n",
        "\n",
        "prediction = model.predict(input_volume)[0]\n",
        "print(prediction)"
      ],
      "metadata": {
        "id": "BLo4YouWofDK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "DenseNet"
      ],
      "metadata": {
        "id": "MBrYSj8nolNw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = DenseNet201(input_shape=(224,224,3), \n",
        "                         include_top=False,\n",
        "                         weights=\"imagenet\")"
      ],
      "metadata": {
        "id": "Q68UEZHfonuN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in base_model.layers:\n",
        "    layer.trainable=False"
      ],
      "metadata": {
        "id": "KbZpr7ZXo6qz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Building Model\n",
        "\n",
        "model=Sequential()\n",
        "model.add(base_model)\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Flatten())\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(2048,kernel_initializer='he_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1024,kernel_initializer='he_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(4,activation='softmax'))"
      ],
      "metadata": {
        "id": "G52MCynGo-SG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "RKsKoZKdpDF_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Compile \n",
        "\n",
        "OPT = tensorflow.keras.optimizers.Adam(lr = 0.001)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              metrics=[tensorflow.keras.metrics.AUC(name = 'auc')],\n",
        "              optimizer=OPT)"
      ],
      "metadata": {
        "id": "HB2bxEY8pTod"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining Callbacks\n",
        "\n",
        "filepath = './best_weights.hdf5'\n",
        "\n",
        "earlystopping = EarlyStopping(monitor = 'val_auc', \n",
        "                              mode = 'max' , \n",
        "                              patience = 15,\n",
        "                              verbose = 1)\n",
        "\n",
        "checkpoint    = ModelCheckpoint(filepath, \n",
        "                                monitor = 'val_auc', \n",
        "                                mode='max', \n",
        "                                save_best_only=True, \n",
        "                                verbose = 1)\n",
        "\n",
        "\n",
        "callback_list = [earlystopping, checkpoint]"
      ],
      "metadata": {
        "id": "r9DgJlp9pXE4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_history=model.fit(train_dataset,\n",
        "                        validation_data=valid_dataset,\n",
        "                        epochs = 500,\n",
        "                        callbacks = callback_list,\n",
        "                        verbose = 1)"
      ],
      "metadata": {
        "id": "X6cj50BapcML"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ensemble Approach"
      ],
      "metadata": {
        "id": "J-uooDD9ook6"
      }
    }
  ]
}