Tumor.ai is an innovative web application designed to empower healthcare professionals with the ability to detect brain tumors in medical MRI images. Tumor.ai integrates the power of deep learning, React, and Flask to deliver an efficient and explainable solution for brain tumor identification.
Tumor.ai is a part of Project PID16, mentored by Prof. Dulani Meedeniya. It aims to provide a seamless user experience for brain tumor identification using an existing deep learning models. The application takes MRI images as input and outputs tumor detection results, including explainability heatmaps.
Key Project Components:
- React Frontend: Building the frontend using React for an interactive user interface.
- Flask Backend: Developing a Flask backend to handle image processing and model predictions.
- Deep Learning Models: Utilizing an existing deep learning models for accurate tumor detection.
Tumor.ai's user-friendly frontend is built using React, offering an intuitive interface for healthcare professionals. The frontend allows users to:
- Upload MRI images for tumor detection.
- View the results, including detection outcomes and explainability heatmaps.
- Interact seamlessly with the application.
Our Flask backend serves as the bridge between the React frontend and the deep learning model. It handles image processing, model inference, and result retrieval. Key functionalities include:
- API endpoints for data upload and prediction retrieval.
- Preprocessing and transformation of uploaded MRI images.
- Seamless integration with the deep learning model.
To get started with Tumor.ai, follow these steps:
- Clone the GitHub repository.
- Configure the Flask Backend for image processing and model integration.
- Build and Deploy the React Frontend for an interactive user interface.
- Run Tumor.ai on your local environment or deploy it to a cloud platform.