GoogleNet.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/eshal26/Leukemia-classification/blob/main/GoogleNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lbM2GrD51tX",
        "outputId": "82c6786a-f8bf-4fff-819b-0529a00915cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  Original.zip\n",
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            "  inflating: Original/Pro/WBC-Malignant-Pro-804.jpg  \n"
          ]
        }
      ],
      "source": [
        "!unzip Original.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets, transforms, models\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "23YddemwIVPe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "\n",
        "# Define paths to your dataset directories\n",
        "dataset_dir = 'Original'\n",
        "benign_dir = os.path.join(dataset_dir, 'Benign')\n",
        "malignant_dir = os.path.join(dataset_dir, 'Malignant')  # Create this directory if it doesn't exist\n",
        "\n",
        "# Create the malignant directory if it doesn't exist\n",
        "if not os.path.exists(malignant_dir):\n",
        "    os.makedirs(malignant_dir)\n",
        "\n",
        "# Move images from early, pre, and pro directories to malignant directory\n",
        "for stage in ['Early', 'Pre', 'Pro']:\n",
        "    stage_dir = os.path.join(dataset_dir, stage)\n",
        "    for img_file in os.listdir(stage_dir):\n",
        "        img_path = os.path.join(stage_dir, img_file)\n",
        "        shutil.move(img_path, malignant_dir)\n",
        "\n",
        "# Update dataset directory structure and labels\n",
        "os.rmdir(os.path.join(dataset_dir, 'Early'))\n",
        "os.rmdir(os.path.join(dataset_dir, 'Pre'))\n",
        "os.rmdir(os.path.join(dataset_dir, 'Pro'))"
      ],
      "metadata": {
        "id": "TqxrzrvgIwbo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Define paths to your original dataset directory\n",
        "original_data_dir = 'Original'\n",
        "benign_dir = os.path.join(original_data_dir, 'Benign')\n",
        "malignant_dir = os.path.join(original_data_dir, 'Malignant')\n",
        "\n",
        "# Define paths to create directories for the new dataset splits\n",
        "train_dir = 'train_dataset'\n",
        "val_dir = 'validation_dataset'\n",
        "test_dir = 'test_dataset'\n",
        "\n",
        "# Create directories for the new dataset splits if they don't exist\n",
        "for directory in [train_dir, val_dir, test_dir]:\n",
        "    os.makedirs(directory, exist_ok=True)\n",
        "    for category in ['Benign', 'Malignant']:\n",
        "        os.makedirs(os.path.join(directory, category), exist_ok=True)\n",
        "\n",
        "# Split dataset and copy images to appropriate directories\n",
        "for category_dir, category in [(benign_dir, 'Benign'), (malignant_dir, 'Malignant')]:\n",
        "    image_files = [os.path.join(category_dir, img) for img in os.listdir(category_dir)]\n",
        "    train_files, test_val_files = train_test_split(image_files, test_size=0.2, random_state=42)\n",
        "    val_files, test_files = train_test_split(test_val_files, test_size=0.5, random_state=42)\n",
        "\n",
        "    for file in train_files:\n",
        "        shutil.copy(file, os.path.join(train_dir, category))\n",
        "    for file in val_files:\n",
        "        shutil.copy(file, os.path.join(val_dir, category))\n",
        "    for file in test_files:\n",
        "        shutil.copy(file, os.path.join(test_dir, category))"
      ],
      "metadata": {
        "id": "kRyl-MgvI6oR"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.RandomResizedCrop(299),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(320),\n",
        "        transforms.CenterCrop(299),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'test': transforms.Compose([\n",
        "        transforms.Resize(320),\n",
        "        transforms.CenterCrop(299),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "}"
      ],
      "metadata": {
        "id": "BpBWwuitJIsI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = {\n",
        "    'train': 'train_dataset',\n",
        "    'val': 'validation_dataset',\n",
        "    'test': 'test_dataset'\n",
        "}\n",
        "\n",
        "image_datasets = {x: datasets.ImageFolder(data_dir[x], data_transforms[x]) for x in ['train', 'val', 'test']}\n",
        "dataloaders = {x: DataLoader(image_datasets[x], batch_size=32, shuffle=True, num_workers=2) for x in ['train', 'val', 'test']}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}\n",
        "class_names = image_datasets['train'].classes\n"
      ],
      "metadata": {
        "id": "7y4c_JjQKr8z"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_ft = models.inception_v3(pretrained=True)\n",
        "\n",
        "# Freeze the layers of the model\n",
        "for param in model_ft.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "# Modify the final layer and the auxiliary layer for binary classification\n",
        "num_ftrs = model_ft.fc.in_features\n",
        "model_ft.fc = nn.Linear(num_ftrs, 1)\n",
        "\n",
        "\n",
        "num_ftrs_aux = model_ft.AuxLogits.fc.in_features\n",
        "model_ft.AuxLogits.fc = nn.Linear(num_ftrs_aux, 1)  # Auxiliary output layer for binary classification\n",
        "\n",
        "# Set device to GPU if available\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "\n",
        "model_ft = model_ft.to(device)"
      ],
      "metadata": {
        "id": "eQkWUj0GLEpj",
        "outputId": "88344ffe-eb3c-4560-ca27-1056b4fdb6f6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=Inception_V3_Weights.IMAGENET1K_V1`. You can also use `weights=Inception_V3_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.BCEWithLogitsLoss()\n",
        "optimizer = optim.Adam(model_ft.fc.parameters(), lr=0.001, weight_decay=0.0001)\n"
      ],
      "metadata": {
        "id": "1zmz-E_7LhhG"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "\n",
        "# Training function\n",
        "def train_model(model, criterion, optimizer,  train_loader, val_loader, num_epochs=10):\n",
        "    train_acc_history = []\n",
        "    val_acc_history = []\n",
        "    train_precision_history = []\n",
        "    val_precision_history = []\n",
        "    train_recall_history = []\n",
        "    val_recall_history = []\n",
        "    train_f1_history = []\n",
        "    val_f1_history = []\n",
        "\n",
        "    dataset_sizes = {'train': len(train_loader.dataset), 'val': len(val_loader.dataset)}\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print(f'Epoch {epoch}/{num_epochs - 1}')\n",
        "        print('-' * 10)\n",
        "\n",
        "        for phase in ['train', 'val']:\n",
        "            if phase == 'train':\n",
        "                model.train()\n",
        "            else:\n",
        "                model.eval()\n",
        "\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "            all_labels = []\n",
        "            all_preds = []\n",
        "\n",
        "            for inputs, labels in (train_loader if phase == 'train' else val_loader):\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device).float()\n",
        "\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                with torch.set_grad_enabled(phase == 'train'):\n",
        "                    if phase == 'train':\n",
        "                        outputs, aux_outputs = model(inputs)\n",
        "                        outputs = outputs.squeeze()\n",
        "                        aux_outputs = aux_outputs.squeeze()\n",
        "                        loss1 = criterion(outputs, labels)\n",
        "                        loss2 = criterion(aux_outputs, labels)\n",
        "                        loss = loss1 + 0.4 * loss2\n",
        "                    else:\n",
        "                        outputs = model(inputs)\n",
        "                        outputs = outputs.squeeze()\n",
        "                        loss = criterion(outputs, labels)\n",
        "\n",
        "                    preds = torch.sigmoid(outputs) > 0.5\n",
        "                    all_labels.extend(labels.cpu().numpy())\n",
        "                    all_preds.extend(preds.cpu().numpy())\n",
        "\n",
        "                    if phase == 'train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "            if phase == 'train':\n",
        "                scheduler.step()\n",
        "\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "            precision = precision_score(all_labels, all_preds, average='binary')\n",
        "            recall = recall_score(all_labels, all_preds, average='binary')\n",
        "            f1 = f1_score(all_labels, all_preds, average='binary')\n",
        "\n",
        "            if phase == 'train':\n",
        "                train_acc_history.append(epoch_acc)\n",
        "                train_precision_history.append(precision)\n",
        "                train_recall_history.append(recall)\n",
        "                train_f1_history.append(f1)\n",
        "            else:\n",
        "                val_acc_history.append(epoch_acc)\n",
        "                val_precision_history.append(precision)\n",
        "                val_recall_history.append(recall)\n",
        "                val_f1_history.append(f1)\n",
        "\n",
        "            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f} Precision: {precision:.4f} Recall: {recall:.4f} F1-score: {f1:.4f}')\n",
        "\n",
        "    return model, train_acc_history, val_acc_history, train_precision_history, val_precision_history, train_recall_history, val_recall_history, train_f1_history, val_f1_history\n",
        "\n",
        "# Train the model\n",
        "model_ft, train_acc_history, val_acc_history, train_precision_history, val_precision_history, train_recall_history, val_recall_history, train_f1_history, val_f1_history = train_model(model_ft, criterion, optimizer, dataloaders['train'], dataloaders['val'], num_epochs=20)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZHrotrGMKUh",
        "outputId": "2568233e-3a9c-41af-9eab-a4cf21f787d5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0/19\n",
            "----------\n",
            "train Loss: 0.6120 Acc: 0.8664 Precision: 0.8649 Recall: 0.9977 F1-score: 0.9266\n",
            "val Loss: 0.2664 Acc: 0.8677 Precision: 0.8648 Recall: 1.0000 F1-score: 0.9275\n",
            "Epoch 1/19\n",
            "----------\n",
            "train Loss: 0.5659 Acc: 0.8909 Precision: 0.8907 Recall: 0.9927 F1-score: 0.9390\n",
            "val Loss: 0.2310 Acc: 0.8862 Precision: 0.8814 Recall: 1.0000 F1-score: 0.9370\n",
            "Epoch 2/19\n",
            "----------\n",
            "train Loss: 0.5392 Acc: 0.9071 Precision: 0.9107 Recall: 0.9868 F1-score: 0.9472\n",
            "val Loss: 0.2072 Acc: 0.9477 Precision: 0.9479 Recall: 0.9927 F1-score: 0.9698\n",
            "Epoch 3/19\n",
            "----------\n",
            "train Loss: 0.5160 Acc: 0.9167 Precision: 0.9218 Recall: 0.9850 F1-score: 0.9523\n",
            "val Loss: 0.1812 Acc: 0.9323 Precision: 0.9259 Recall: 1.0000 F1-score: 0.9615\n",
            "Epoch 4/19\n",
            "----------\n",
            "train Loss: 0.5065 Acc: 0.9228 Precision: 0.9277 Recall: 0.9855 F1-score: 0.9557\n",
            "val Loss: 0.1636 Acc: 0.9415 Precision: 0.9384 Recall: 0.9964 F1-score: 0.9665\n",
            "Epoch 5/19\n",
            "----------\n",
            "train Loss: 0.4886 Acc: 0.9313 Precision: 0.9362 Recall: 0.9859 F1-score: 0.9604\n",
            "val Loss: 0.2067 Acc: 0.9631 Precision: 0.9817 Recall: 0.9745 F1-score: 0.9781\n",
            "Epoch 6/19\n",
            "----------\n",
            "train Loss: 0.4895 Acc: 0.9351 Precision: 0.9410 Recall: 0.9850 F1-score: 0.9625\n",
            "val Loss: 0.1586 Acc: 0.9569 Precision: 0.9579 Recall: 0.9927 F1-score: 0.9750\n",
            "Epoch 7/19\n",
            "----------\n",
            "train Loss: 0.4706 Acc: 0.9420 Precision: 0.9464 Recall: 0.9873 F1-score: 0.9664\n",
            "val Loss: 0.1516 Acc: 0.9662 Precision: 0.9714 Recall: 0.9891 F1-score: 0.9802\n",
            "Epoch 8/19\n",
            "----------\n",
            "train Loss: 0.4829 Acc: 0.9320 Precision: 0.9466 Recall: 0.9746 F1-score: 0.9604\n",
            "val Loss: 0.1450 Acc: 0.9385 Precision: 0.9322 Recall: 1.0000 F1-score: 0.9649\n",
            "Epoch 9/19\n",
            "----------\n",
            "train Loss: 0.4934 Acc: 0.9313 Precision: 0.9396 Recall: 0.9818 F1-score: 0.9602\n",
            "val Loss: 0.1274 Acc: 0.9538 Precision: 0.9514 Recall: 0.9964 F1-score: 0.9734\n",
            "Epoch 10/19\n",
            "----------\n",
            "train Loss: 0.4753 Acc: 0.9389 Precision: 0.9455 Recall: 0.9846 F1-score: 0.9646\n",
            "val Loss: 0.1605 Acc: 0.9692 Precision: 0.9818 Recall: 0.9818 F1-score: 0.9818\n",
            "Epoch 11/19\n",
            "----------\n",
            "train Loss: 0.4797 Acc: 0.9309 Precision: 0.9454 Recall: 0.9746 F1-score: 0.9597\n",
            "val Loss: 0.1203 Acc: 0.9508 Precision: 0.9512 Recall: 0.9927 F1-score: 0.9715\n",
            "Epoch 12/19\n",
            "----------\n",
            "train Loss: 0.4704 Acc: 0.9363 Precision: 0.9480 Recall: 0.9782 F1-score: 0.9629\n",
            "val Loss: 0.1315 Acc: 0.9723 Precision: 0.9750 Recall: 0.9927 F1-score: 0.9838\n",
            "Epoch 13/19\n",
            "----------\n",
            "train Loss: 0.4647 Acc: 0.9374 Precision: 0.9513 Recall: 0.9759 F1-score: 0.9634\n",
            "val Loss: 0.1152 Acc: 0.9569 Precision: 0.9547 Recall: 0.9964 F1-score: 0.9751\n",
            "Epoch 14/19\n",
            "----------\n",
            "train Loss: 0.4669 Acc: 0.9378 Precision: 0.9501 Recall: 0.9777 F1-score: 0.9637\n",
            "val Loss: 0.1238 Acc: 0.9754 Precision: 0.9785 Recall: 0.9927 F1-score: 0.9856\n",
            "Epoch 15/19\n",
            "----------\n",
            "train Loss: 0.4637 Acc: 0.9382 Precision: 0.9489 Recall: 0.9796 F1-score: 0.9640\n",
            "val Loss: 0.1178 Acc: 0.9692 Precision: 0.9715 Recall: 0.9927 F1-score: 0.9820\n",
            "Epoch 16/19\n",
            "----------\n",
            "train Loss: 0.4530 Acc: 0.9474 Precision: 0.9562 Recall: 0.9827 F1-score: 0.9693\n",
            "val Loss: 0.1100 Acc: 0.9662 Precision: 0.9681 Recall: 0.9927 F1-score: 0.9803\n",
            "Epoch 17/19\n",
            "----------\n",
            "train Loss: 0.4719 Acc: 0.9366 Precision: 0.9485 Recall: 0.9782 F1-score: 0.9631\n",
            "val Loss: 0.1270 Acc: 0.9508 Precision: 0.9481 Recall: 0.9964 F1-score: 0.9716\n",
            "Epoch 18/19\n",
            "----------\n",
            "train Loss: 0.4555 Acc: 0.9459 Precision: 0.9558 Recall: 0.9814 F1-score: 0.9684\n",
            "val Loss: 0.1047 Acc: 0.9662 Precision: 0.9681 Recall: 0.9927 F1-score: 0.9803\n",
            "Epoch 19/19\n",
            "----------\n",
            "train Loss: 0.4501 Acc: 0.9516 Precision: 0.9621 Recall: 0.9814 F1-score: 0.9717\n",
            "val Loss: 0.1049 Acc: 0.9785 Precision: 0.9820 Recall: 0.9927 F1-score: 0.9873\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_training_results(train_acc_history, val_acc_history):\n",
        "    train_acc_history = [x.cpu().numpy() for x in train_acc_history]\n",
        "    val_acc_history = [x.cpu().numpy() for x in val_acc_history]\n",
        "\n",
        "    epochs = range(1, len(train_acc_history) + 1)\n",
        "\n",
        "    plt.figure(figsize=(10, 5))\n",
        "\n",
        "    # Plot training and validation accuracy\n",
        "    plt.plot(epochs, train_acc_history, 'bo-', label='Training accuracy')\n",
        "    plt.plot(epochs, val_acc_history, 'ro-', label='Validation accuracy')\n",
        "    plt.title('Training and Validation Accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "    plt.grid()\n",
        "    plt.show()\n",
        "\n",
        "# Assuming train_acc_history and val_acc_history are already defined\n",
        "plot_training_results(train_acc_history, val_acc_history)\n"
      ],
      "metadata": {
        "id": "0g3rMpM5CtGG",
        "outputId": "924f5d3a-b18e-4be4-922c-2d0f517a18eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "def evaluate_model_with_detailed_metrics(model, dataloader, criterion, device):\n",
        "    model.eval()\n",
        "    test_loss = 0.0\n",
        "    all_labels = []\n",
        "    all_preds = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in dataloader:\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "\n",
        "            # Apply sigmoid function and threshold to get binary predictions\n",
        "            outputs = outputs.squeeze()  # Remove extra dimension for binary classification\n",
        "            preds = torch.sigmoid(outputs) > 0.5  # Convert to binary predictions\n",
        "\n",
        "            # Convert predictions and labels to numpy for metrics calculation\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "            all_preds.extend(preds.cpu().numpy())\n",
        "\n",
        "            # Compute loss\n",
        "            loss = criterion(outputs, labels.float())\n",
        "            test_loss += loss.item() * inputs.size(0)\n",
        "\n",
        "    test_loss /= len(dataloader.dataset)\n",
        "    accuracy = accuracy_score(all_labels, all_preds)\n",
        "    precision = precision_score(all_labels, all_preds, average='weighted')\n",
        "    recall = recall_score(all_labels, all_preds, average='weighted')\n",
        "    f1 = f1_score(all_labels, all_preds, average='weighted')\n",
        "\n",
        "    print(f'Test Loss: {test_loss:.4f}')\n",
        "    print(f'Test Accuracy: {accuracy:.4f}')\n",
        "    print(f'Test Precision: {precision:.4f}')\n",
        "    print(f'Test Recall: {recall:.4f}')\n",
        "    print(f'Test F1-score: {f1:.4f}')\n",
        "\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(all_labels, all_preds)\n",
        "    print(\"Confusion Matrix:\\n\", cm)\n",
        "\n",
        "    # Plot confusion matrix\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Benign', 'Malignant'])\n",
        "    disp.plot(cmap=plt.cm.Blues)\n",
        "    plt.show()\n",
        "\n",
        "    # Print classification report\n",
        "    class_report = classification_report(all_labels, all_preds, target_names=['Benign', 'Malignant'])\n",
        "    print(\"Classification Report:\\n\", class_report)\n",
        "\n",
        "    return test_loss, accuracy, precision, recall, f1, cm, class_report\n",
        "\n",
        "# Example usage\n",
        "test_loss, test_accuracy, test_precision, test_recall, test_f1, cm, class_report = evaluate_model_with_detailed_metrics(model_ft, dataloaders['test'], criterion, device)\n"
      ],
      "metadata": {
        "id": "Bp6LTJ98IjoF",
        "outputId": "4e2c7b88-f569-496a-bc7d-840e444d593a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0984\n",
            "Test Accuracy: 0.9725\n",
            "Test Precision: 0.9721\n",
            "Test Recall: 0.9725\n",
            "Test F1-score: 0.9721\n",
            "Confusion Matrix:\n",
            " [[ 45   6]\n",
            " [  3 273]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "      Benign       0.94      0.88      0.91        51\n",
            "   Malignant       0.98      0.99      0.98       276\n",
            "\n",
            "    accuracy                           0.97       327\n",
            "   macro avg       0.96      0.94      0.95       327\n",
            "weighted avg       0.97      0.97      0.97       327\n",
            "\n"
          ]
        }
      ]
    }
  ]
}