VGG16_Colorized_Transfer_best_model

Brain Tumor Classification using VGG16 (Colorized MRI)

This repository contains a VGG16 transfer learning model trained on enhanced colorized MRI images for automated brain tumor classification.

🧠 Tumor Classes

• Glioma
• Meningioma
• Pituitary

📊 Model Performance

• Test Accuracy: 88.70%
• Framework: PyTorch
• Architecture: VGG16 (Transfer Learning)
• Pre-trained on: ImageNet
• Input Size: 224×224 RGB
• Number of Classes: 3

🎨 Colorization Strategy

MRI images were enhanced using CLAHE and converted into multiple colormap representations to study the impact of color information on classification performance.

🏆 Best Model Checkpoint

represents the best-performing checkpoint, saved at peak validation accuracy.

🔬 Training Highlights

• Transfer learning with frozen convolution layers
• Fine-tuned classifier head
• Data augmentation
• Stratified train/validation/test split (70/15/15)
• Early stopping and learning rate scheduling

⚠️ Disclaimer

This model is intended strictly for research and educational purposes and must not be used for clinical diagnosis or treatment planning.

👤 Author

Prashant Parwani

The uploaded file: