Brain-Tumor-Detection

Welcome to the Brain Tumor Detection project !

About

• This project is a Flask-based web application designed for brain tumor detection. Healthcare professionals can effortlessly upload MRI scans and receive instant results. Our application is not only fast and accurate but also offers a user-friendly interface, powered by cutting-edge deep learning technology.

• This Project was built at The Actual Open AI Hackathon

• This Project was built at Evergreen Grand Hackathon

How to Contribute

We welcome contributions from the community to make this project even more amazing! Here's how you can get involved:

• Fork the repository.
• Create a feature branch: git checkout -b feature-name.
• Make your enhancements and improvements.
• Test thoroughly.
• Submit a pull request.

For more details on contributing, please see our Contribution Guidelines.

Features

Model Training and Integration

• Train a brain tumor detection model using a suitable deep learning architecture (e.g., U-Net, CNN).
• Save the trained model weights and architecture for later use.

Setting Up Flask App

• Create a new directory for your Flask project.
• Set up the basic structure: static/ for static files (e.g., CSS, images), templates/ for HTML templates.

Flask Routing

• Define routes in app.py to handle different pages of the web application (e.g., home page, result page).

HTML Templates

• Create HTML templates in the templates/ directory. These templates will define the structure of your web pages, including forms for uploading MRI scans.

File Upload and Processing

• Implement a form in your HTML template to allow users to upload MRI scans.
• In the Flask route that handles form submission, process the uploaded file. This may involve saving the file, preprocessing it for model input, and passing it to the model for prediction.

Model Inference

• Load the pre-trained model in Flask.
• Use it to make predictions on the uploaded MRI scan.

Display Results

• Create a separate template to display the results of the tumor detection.

Styling and User Interface

• Use CSS and other front-end technologies to style your web application and make it user-friendly.

Integration of Model Output

• Display the model's output on the results page. This could be a binary classification (tumor or no tumor) or a segmentation result.

Testing and Debugging

• Thoroughly test the application, including edge cases. Use Flask's built-in debugging tools to identify and fix any issues.

Deployment

• Once your application is working locally, you can deploy it using a platform like Heroku or AWS.

Documentation and Usage Guide

• Provide clear instructions on how to use the web application, including any dependencies or setup required.
• Remember to handle potential errors gracefully, such as incorrect file types or unexpected inputs.
• Consider adding features like user authentication and security measures to protect sensitive medical data.
• Always respect privacy and data protection regulations when working with medical information.

Getting Started

To run this application locally, follow these steps:

1. Clone this repository.
2. Set up a virtual environment and install the required dependencies.
3. Run the Flask application.
4. Access the application in your web browser.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Thank you for your interest in our Brain Tumor Detection project! We look forward to your contributions and hope this tool can make a positive impact in healthcare.