🧠 NeuroScan AI - Brain Tumor Detection & Neural Explainability

📊 Fine-Tuning Performance & Results

Fine-Tuning Improvements Applied:

1. Top-Block Unfreezing: Unfroze Block 5 layers (block5_conv1 to block5_conv4) of VGG-19 to adapt high-level texture features specifically to brain MRI tissue contrast.
2. Optimized Learning Rate: Used Adam(lr=5e-5) with ReduceLROnPlateau for stable convergence.
3. Batch Size Tuning: Reduced batch size from 64 to 32 on 1,445 training samples to double gradient update frequency per epoch.
4. Regularization: Increased Dropout to 0.3 to eliminate overfitting and boost test set generalizability.

✨ Features

• ⚡ Dynamic Model Hot-Reloading: Flask backend monitors model.h5 modification timestamp on disk and hot-reloads model weights seamlessly without server downtime.
• 🎯 Grad-CAM Visual Heatmaps: Generates neural activation heatmaps from block5_conv4 overlaid onto brain MRI scans using OpenCV JET colormaps.
• 🎨 Modern Medical Dashboard:
  • Analysis Tab: Drag-and-drop MRI scan loading, preview, SVG confidence gauge ring, and Grad-CAM heatmap visualization.
  • Reports Tab: Interactive diagnostic log table with search filtering, summary stats (Total Scans, Tumor Count, Avg Confidence), and side-by-side report detail modal.
  • Settings Tab: Customizable Grad-CAM target layers, colormaps (JET, VIRIDIS, HOT, INFERNO), alpha transparency slider, and manual weight reload triggers.

🛠️ Project Structure

Brain Tumor/
├── app.py                      # Flask Server with Grad-CAM & Weight Hot-Reloading
├── brain_tumor_dataset/        # Training script (train_brain_tumor.py)
├── templates/
│   └── index.html              # NeuroScan AI Dashboard UI
├── static/
│   ├── css/style.css           # Vanilla CSS3 Dark Medical Theme
│   └── js/app.js               # Frontend Controller & REST API Handlers
├── Detection.py                # Preprocessing & evaluation utilities
├── training_performance.png    # Training vs Validation Accuracy/Loss plot
├── README.md                   # Project documentation
└── .gitignore                  # Git ignore specifications

🚀 How to Run Locally

1. Prerequisites

2. Launch Backend Application

3. Open Dashboard