🩺 Breast Cancer Image Classification with ResNet50

This project is a binary image classifier that distinguishes between healthy and sick tissue samples from histopathological images using a fine-tuned ResNet50 model in PyTorch.

📁 Dataset

• Images are organized in the train/ folder with two subfolders:
  • train/Healthy/
  • train/Sick/
• The original dataset is split into:
  • 80% training
  • 20% validation
  • Seperate testing data

🧠 Model Architecture

• Base model: ResNet50 (pretrained on ImageNet)
• Final FC layer modified to output a single logit for binary classification
• Loss function: BCEWithLogitsLoss
• Optimizer: Adam
• Scheduler: StepLR

🔁 Training Details

• Image size: 224x224
• Batch size: 32
• Data augmentation:
  • Random horizontal flip
  • Random rotation
• Normalization: Mean and std set to [0.5, 0.5, 0.5]
• Early stopping with patience = 5
• Training runs for max 20 epochs

✅ Evaluation

• Accuracy is calculated on the held-out test set
• Test predictions are thresholded at 0.5 after applying sigmoid
• Final test accuracy is printed after training