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