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--- |
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title: MRI Brain Tumor Detection |
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emoji: 🧠 |
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colorFrom: blue |
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colorTo: purple |
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sdk: docker |
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app_port: 7860 |
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pinned: false |
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--- |
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# 🧠 MRI Brain Tumor Detection System |
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Deep Learning application for automated brain tumor classification from MRI scans using a custom ResidualInceptionBlock CNN architecture. |
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## 🎯 Features |
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- **4-Class Classification**: Glioma, Meningioma, Pituitary, No Tumor |
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- **Real-time Inference**: Fast predictions with confidence scores |
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- **Modern UI**: Clean, responsive React interface |
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- **RESTful API**: FastAPI backend with automatic documentation |
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## 🏗️ Architecture |
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- **Frontend**: React 18 + Vite |
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- **Backend**: FastAPI + PyTorch |
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- **Model**: Custom ResidualInceptionBlock CNN (50+ layers) |
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- **Deployment**: Docker + Hugging Face Spaces |
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## 🚀 Quick Start |
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### Using the Deployed App |
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Simply visit the app URL and upload an MRI scan image to get instant predictions. |
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### Local Development |
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1. **Clone the repository** |
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```bash |
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https://huggingface.co/spaces/arghadip2002/NeuroGuard-Web-Application |
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cd NeuroGuard-Web-Application |
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``` |
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2. **Start with Docker Compose** |
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```bash |
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docker-compose up --build |
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``` |
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3. **Access the application** |
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- Frontend: http://localhost:3000 |
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- API Docs: http://localhost:8000/docs |
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### Manual Setup |
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**Backend:** |
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```bash |
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cd backend |
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pip install -r requirements.txt |
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uvicorn app.main:app --reload |
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``` |
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**Frontend:** |
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```bash |
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cd frontend |
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npm install |
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npm run dev |
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``` |
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## 📋 API Endpoints |
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- `POST /api/predict` - Upload MRI image for prediction |
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- `GET /health` - Health check endpoint |
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- `GET /docs` - Interactive API documentation |
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## 🎨 Usage |
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1. Upload an MRI brain scan (PNG, JPG, JPEG) |
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2. Click "Run Diagnosis" |
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3. View prediction with confidence score |
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## 📊 Model Information |
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- **Classes**: 4 (Glioma, Meningioma, Pituitary, No Tumor) |
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- **Input Size**: 224x224 RGB images |
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- **Architecture**: Custom ResidualInceptionBlock with 50+ layers |
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## 🛠️ Technology Stack |
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- **PyTorch** 2.1.0 |
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- **FastAPI** 0.104.1 |
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- **React** 18.2.0 |
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- **Vite** 5.0.0 |
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- **Docker** & Docker Compose |
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## 📝 License |
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MIT LICENSE |
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## 👨💻 Author |
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Arghadip Biswas and Sayan Das |
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## 📊 Dataset |
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- Dataset: https://github.com/arghadip2002/SAETCN-and-SASNET-Architectures/blob/main/dataLinks |
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- Based on SAETCN architecture |
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## 📜 Citation & Academic Acknowledgment |
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This repository provides the **NeuroGuard Web Application**, which is the deployment of the novel **SAETCN** and **SAS-Net** architectures detailed in our research paper. |
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If you use this deployed software or its code in your academic work, please cite the underlying paper to acknowledge the methodology and results: |
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### ✍️ Preferred Citation (BibTeX) |
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```bibtex |
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@article{das2025novel, |
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title={Novel Deep Learning Architectures for Classification and Segmentation of Brain Tumors from MRI Images}, |
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author={Das, Sayan and Biswas, Arghadip}, |
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year={2025}, |
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eprint={2512.06531}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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#### 🔗 Paper Link |
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The full paper is publicly available on the arXiv preprint server: [arXiv:2512.06531](https://www.arxiv.org/abs/2512.06531) |
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###### ⭐ Note: Citing the paper is essential for the advancement of open science and ensures proper credit for the research that powers this application. Thank you for your support! |
