Visit aichestxray.kanasva.me
This project utilises Next.js 14 as a full-stack framework to deliver diagnoses for 18 chest X-ray pathologies using TorchXRayVision, with the model deployed separately on AWS Lambda, see ai-chest-xray-diagnosis-api
- Diagnosis for 18 chest X-ray pathologies, including Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Enlarged Cardiomediastinum, Fibrosis, Fracture, Hernia, Infiltration, Lung Lesion, Lung Opacity, Mass, Nodule, Pleural Thickening, Pneumonia, and Pneumothorax.
- Detection of out-of-distribution images using an auto-encoder to prevent predictions on images that are different from the training data.
- Grad-CAM visualisation to highlight regions of interest in X-ray images
- GitHub OAuth authentication
- Global and user-specific quotas
- TorchXRayVision - For chest X-ray analysis.
- Next.js - Framework for full-stack development.
- React - Front-end library for building user interfaces.
- React Aria - For accessible and reusable UI components.
- Tailwind CSS - Fast and easy CSS styling.
- Lucia Auth - Authentication library.
- ZSA - For React server actions.
- Drizzle ORM - For database interactions.
- This project follows the Material Design 3 guidelines, especially the colour system.
This project uses a layered architecture to ensure modularity, maintainability, and scalability:
- User Interface: The front-end layer where users interact with the application.
- Server Actions: The backend layer to handle API interactions.
- Use Cases: The layer for business logic and application-specific functionality, such as quota checking.
- Data Access: The layer that manages data retrieval and storage operations.
- Improve error handling
- Google Login that needs to submit app verification