ResNet152.ipynb

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      "authorship_tag": "ABX9TyMmOszXakf/kgV76MTmeWBB",
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    "language_info": {
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    "accelerator": "GPU"
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  "cells": [
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        "<a href=\"https://colab.research.google.com/github/eshal26/Leukemia-classification/blob/main/ResNet152.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          ]
        }
      ],
      "source": [
        "!unzip Original.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision import datasets\n",
        "from torch.utils.data import DataLoader\n",
        "import os\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "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'))\n"
      ],
      "metadata": {
        "id": "pvFjueDmCAkp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\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))\n"
      ],
      "metadata": {
        "id": "24lPIQGfCKYV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Define paths to your dataset directories\n",
        "train_dir = 'train_dataset'\n",
        "val_dir = 'validation_dataset'\n",
        "test_dir = 'test_dataset'\n",
        "\n",
        "# Define transformations for training, validation, and testing data\n",
        "train_transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "val_test_transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Create datasets\n",
        "train_dataset = torchvision.datasets.ImageFolder(root=train_dir, transform=train_transform)\n",
        "val_dataset = torchvision.datasets.ImageFolder(root=val_dir, transform=val_test_transform)\n",
        "test_dataset = torchvision.datasets.ImageFolder(root=test_dir, transform=val_test_transform)\n",
        "\n",
        "# Create data loaders\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "hQ0oPc1sCQXc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataloaders = {'train': train_loader, 'val': val_loader, 'test': test_loader}\n",
        "dataset_sizes = {'train': len(train_dataset), 'val': len(val_dataset), 'test': len(test_dataset)}\n",
        "class_names = train_dataset.classes"
      ],
      "metadata": {
        "id": "XMTUu3sdDffA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "from torchvision import models\n",
        "\n",
        "# Load ResNet-152 model pre-trained on ImageNet\n",
        "model_resnet152 = models.resnet152(pretrained=True)\n",
        "\n",
        "# Modify the final layer to match the number of classes\n",
        "num_features = model_resnet152.fc.in_features\n",
        "model_resnet152.fc = nn.Linear(num_features, 2)\n",
        "\n",
        "model_resnet152 = model_resnet152.to(device)\n"
      ],
      "metadata": {
        "id": "_0SAMXgYCT_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Use a pre-trained optimizer\n",
        "optimizer = optim.SGD(model_resnet152.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "# Learning rate scheduler\n",
        "from torch.optim import lr_scheduler\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n"
      ],
      "metadata": {
        "id": "66LjjOLNDILO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    train_loss_history = []\n",
        "    val_loss_history = []\n",
        "    train_acc_history = []\n",
        "    val_acc_history = []\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print(f'Epoch {epoch}/{num_epochs - 1}')\n",
        "        print('-' * 10)\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in ['train', 'val']:\n",
        "            if phase == 'train':\n",
        "                model.train()  # Set model to training mode\n",
        "            else:\n",
        "                model.eval()   # Set model to evaluate mode\n",
        "\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterate over data\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "                # Zero the parameter gradients\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # Forward\n",
        "                # Track history if only in train\n",
        "                with torch.set_grad_enabled(phase == 'train'):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "\n",
        "                    # Backward + optimize only if in training phase\n",
        "                    if phase == 'train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # Statistics\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",
        "\n",
        "            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n",
        "\n",
        "            if phase == 'train':\n",
        "                train_loss_history.append(epoch_loss)\n",
        "                train_acc_history.append(epoch_acc.item())\n",
        "            else:\n",
        "                val_loss_history.append(epoch_loss)\n",
        "                val_acc_history.append(epoch_acc.item())\n",
        "\n",
        "            # Deep copy the model\n",
        "            if phase == 'val' and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "        print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n",
        "    print(f'Best val Acc: {best_acc:.4f}')\n",
        "\n",
        "    # Load best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "\n",
        "    # Plot accuracy and loss over epochs\n",
        "    plot_metrics(train_loss_history, val_loss_history, train_acc_history, val_acc_history)\n",
        "\n",
        "    return model\n",
        "\n",
        "def plot_metrics(train_loss, val_loss, train_acc, val_acc):\n",
        "    epochs = range(len(train_loss))\n",
        "\n",
        "    plt.figure(figsize=(12, 4))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(epochs, train_loss, label='Training Loss')\n",
        "    plt.plot(epochs, val_loss, label='Validation Loss')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.title('Loss Over Epochs')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(epochs, train_acc, label='Training Accuracy')\n",
        "    plt.plot(epochs, val_acc, label='Validation Accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.title('Accuracy Over Epochs')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Train the model and plot metrics\n",
        "model_resnet152 = train_model(model_resnet152, criterion, optimizer, exp_lr_scheduler, num_epochs=15)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "81H1TMUoDMC2",
        "outputId": "196db6cd-b360-48d1-ecdd-5d066a1d1da3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0/14\n",
            "----------\n",
            "train Loss: 0.1670 Acc: 0.9393\n",
            "val Loss: 0.0508 Acc: 0.9908\n",
            "\n",
            "Epoch 1/14\n",
            "----------\n",
            "train Loss: 0.0236 Acc: 0.9939\n",
            "val Loss: 0.0153 Acc: 0.9938\n",
            "\n",
            "Epoch 2/14\n",
            "----------\n",
            "train Loss: 0.0095 Acc: 0.9985\n",
            "val Loss: 0.0138 Acc: 0.9938\n",
            "\n",
            "Epoch 3/14\n",
            "----------\n",
            "train Loss: 0.0076 Acc: 0.9981\n",
            "val Loss: 0.0154 Acc: 0.9938\n",
            "\n",
            "Epoch 4/14\n",
            "----------\n",
            "train Loss: 0.0044 Acc: 0.9996\n",
            "val Loss: 0.0123 Acc: 0.9938\n",
            "\n",
            "Epoch 5/14\n",
            "----------\n",
            "train Loss: 0.0074 Acc: 0.9977\n",
            "val Loss: 0.0152 Acc: 0.9969\n",
            "\n",
            "Epoch 6/14\n",
            "----------\n",
            "train Loss: 0.0024 Acc: 1.0000\n",
            "val Loss: 0.0113 Acc: 0.9938\n",
            "\n",
            "Epoch 7/14\n",
            "----------\n",
            "train Loss: 0.0026 Acc: 0.9996\n",
            "val Loss: 0.0118 Acc: 0.9969\n",
            "\n",
            "Epoch 8/14\n",
            "----------\n",
            "train Loss: 0.0019 Acc: 1.0000\n",
            "val Loss: 0.0120 Acc: 0.9969\n",
            "\n",
            "Epoch 9/14\n",
            "----------\n",
            "train Loss: 0.0024 Acc: 1.0000\n",
            "val Loss: 0.0127 Acc: 0.9969\n",
            "\n",
            "Epoch 10/14\n",
            "----------\n",
            "train Loss: 0.0017 Acc: 1.0000\n",
            "val Loss: 0.0119 Acc: 0.9938\n",
            "\n",
            "Epoch 11/14\n",
            "----------\n",
            "train Loss: 0.0018 Acc: 1.0000\n",
            "val Loss: 0.0122 Acc: 0.9938\n",
            "\n",
            "Epoch 12/14\n",
            "----------\n",
            "train Loss: 0.0015 Acc: 1.0000\n",
            "val Loss: 0.0114 Acc: 0.9969\n",
            "\n",
            "Epoch 13/14\n",
            "----------\n",
            "train Loss: 0.0022 Acc: 1.0000\n",
            "val Loss: 0.0114 Acc: 0.9969\n",
            "\n",
            "Epoch 14/14\n",
            "----------\n",
            "train Loss: 0.0017 Acc: 1.0000\n",
            "val Loss: 0.0118 Acc: 0.9938\n",
            "\n",
            "Training complete in 15m 20s\n",
            "Best val Acc: 0.9969\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model_resnet152.state_dict(), 'resnet152_best_model.pth')\n"
      ],
      "metadata": {
        "id": "0ysvZ4MrE7Zc"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn.metrics import classification_report\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",
        "            loss = criterion(outputs, labels)\n",
        "            test_loss += loss.item() * inputs.size(0)\n",
        "\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "            all_preds.extend(predicted.cpu().numpy())\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)\n",
        "    disp.plot(cmap=plt.cm.Blues)\n",
        "    plt.show()\n",
        "\n",
        "    # Print classification report\n",
        "    class_report = classification_report(all_labels, all_preds)\n",
        "    print(\"Classification Report:\\n\", class_report)\n",
        "\n",
        "    return test_loss, accuracy, precision, recall, f1, cm, class_report\n",
        "\n",
        "test_loss_resnet152, test_accuracy_resnet152, test_precision_resnet152, test_recall_resnet152, test_f1_resnet152, cm_resnet152, class_report_resnet152 = evaluate_model_with_detailed_metrics(model_resnet152, dataloaders['test'], criterion, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        },
        "id": "yPkSg_t-FBtN",
        "outputId": "64a8a717-8358-4389-84ff-3c3ff33ed417"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0050\n",
            "Test Accuracy: 1.0000\n",
            "Test Precision: 1.0000\n",
            "Test Recall: 1.0000\n",
            "Test F1-score: 1.0000\n",
            "Confusion Matrix:\n",
            " [[ 51   0]\n",
            " [  0 276]]\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",
            "           0       1.00      1.00      1.00        51\n",
            "           1       1.00      1.00      1.00       276\n",
            "\n",
            "    accuracy                           1.00       327\n",
            "   macro avg       1.00      1.00      1.00       327\n",
            "weighted avg       1.00      1.00      1.00       327\n",
            "\n"
          ]
        }
      ]
    }
  ]
}