Research on grading detection methods for diabetic retinopathy based on deep learning.

Abstract

Objective: To design a deep learning-based model for early screening of diabetic retinopathy, predict the condition, and provide interpretable justifications.

Methods: The experiment's model structure is designed based on the Vision Transformer architecture which was initiated in March 2023 and the first version was produced in July 2023 at Affiliated Hospital of Hangzhou Normal University. We use the publicly available EyePACS dataset as input to train the model. Using the trained model, we predict whether a given patient's fundus images indicate diabetic retinopathy and provide the relevant affected areas as the basis for the judgement.

Results: The model was validated using two subsets of the IDRiD dataset. Our model not only achieved good results in terms of detection accuracy, reaching around 0.88, but also performed comparably to similar models annotated for affected areas in predicting the affected regions.

Conclusion: Utilizing image-level annotations, we implemented a method for detecting diabetic retinopathy through deep learning and provided interpretable justifications to assist clinicians in diagnosis.