Efficient Diabetic Retinopathy Detection System Using Fundus Images with Explainable AI

An efficient diabetic retinopathy detection system that uses fundus images and explains its predictions.

An efficient diabetic retinopathy detection system that uses fundus images and explains its predictions. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with images or video: building a dataset, training or fine-tuning a vision model, measuring its performance and wrapping it in a usable tool.

The main parts are explanations of each result (feature importance or visual evidence), dataset preparation and annotation, model training or fine-tuning, evaluation with standard metrics, plus visual examples, and an interface that works on uploaded images or live video. The reference build uses SHAP, Python, OpenCV, PyTorch, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.