🧠 YOLOv11-Powered Brain Tumor Diagnostic Suite

📄 Official Publication

This project is officially published on Zenodo. You can read the full research paper here: 👉 Read the Paper (DOI: 10.5281/zenodo.18898503)

🚀 Project Overview

An end-to-end medical AI solution for the classification and pixel-level segmentation of brain tumors from MRI scans.

Key Features:

• Architecture: YOLOv11 (Latest Generation).
• Accuracy: 98.7% on a balanced dataset of Glioma, Meningioma, Pituitary, and Healthy scans.
• Gatekeeper Pipeline: A dual-pipeline logic that ensures high-confidence results before clinical visualization.
• Deployment: Real-time web interface built with Streamlit.

🛠️ Installation & Usage

1. Clone the repo: git clone https://github.com/Shabana2002/Brain-Tumor.git
2. Install dependencies: pip install -r requirements.txt
3. Run the App: streamlit run app.py

📊 Results

The model provides high-precision masks and classification labels, reducing the time required for manual radiological review.