The Ocular Disease Identifier leverages AI to detect ocular diseases from fundus images, providing an accessible and innovative solution for healthcare professionals. This tool aims to enhance early diagnosis and treatment planning, ultimately improving patient outcomes.
- Accuracy: Utilizes a high-performing convolutional neural network (CNN) to analyze fundus images effectively.
- Accessibility: Offers a user-friendly web application for seamless interaction with the AI model.
- Real-World Impact: Designed to integrate with clinical workflows, supporting healthcare professionals in making informed decisions.
| Contributor | Expertise & Tools | Notable Contributions |
|---|---|---|
 Kristian Diana Project Lead |
   |
- #20 Integrate all elements for the first project showcase demo - Managing team using agile/scrum methodologies and Kanban boards |
 Alex Chen ML/AI Engineer & Full-Stack Developer |
     |
- Frontend components development - Responsive UI design - Model training - Data preprocessing pipelines |
 Iain Macdonald Full-Stack Developer |
|
- Frontend components development - API integrations - Database integration |
 Jasimraza Momin Cloud/DevOps Engineer |
  |
- CI/CD pipelines - Cloud deployment |
 George Ghiugan Full-Stack Developer |
|
- Model training - Database integration |
 Maheer Huq Full-Stack Developer |
|
- Responsive design - Performance optimization |
 Nick Zajkeskovic Cloud/DevOps Engineer |
|
- Model creation - Cloud deployment |
| Samuel Shi Full-Stack Developer |
|
- UI enhancements - Component-level testing |
| Vikram Chandar ML/AI Engineer |
 |
- Model evaluation and testing - Data augmentation pipelines |
