README.md

metadata

title: Brain Tumor MRI Classifier
emoji: 🔥
colorFrom: purple
colorTo: purple
sdk: gradio
sdk_version: 6.14.0
python_version: '3.13'
app_file: app.py
pinned: false
license: mit
short_description: Brain tumor MRI 4-class classifier with patient-level split

Brain Tumor MRI Classifier

A 4-class brain tumor classifier built with EfficientNet-B3, trained with rigorous patient-level data splitting.

Test accuracy: 95.05% (TTA) on 687 unseen patients Macro AUC: 0.9965 Patient leakage: 0 (verified by set intersection)

This demo lets you upload a brain MRI and see:

• Which of 4 classes the model predicts (glioma, meningioma, no tumor, pituitary)
• Confidence percentages for all 4 classes
• A Grad-CAM heatmap showing where the model focused

⚠️ Medical Disclaimer

This is a portfolio/research demonstration. It must NOT be used for any medical decision-making. The model has not been validated in a clinical setting and has not been reviewed by radiologists.

Why this project is different

Most public brain tumor classifiers use image-level random splits, which leak patient information between train and test sets. This project uses patient-level splitting — no patient's MRI appears in more than one split. The 95.05% accuracy is honest, not inflated.

Built with

• PyTorch + timm (EfficientNet-B3)
• pytorch-grad-cam for interpretability
• Gradio for the web interface

Author

Tanishq Arya — GitHub

Full project details, training code, and analysis on the GitHub repository.