Attention-Guided Residual Learning for Reliable Multi-Class Diabetic Retinopathy Severity Assessment

Abstract

Diabetes is one of the most common metabolic diseases (DM) globally, accompanied by mild to severe secondary complications, including diabetic retinopathy (DR), which can damage the retina and lead to vision loss. DR detection is crucial, as early treatment of DR can effectively prevent vision loss in patients. Image processing systems have been developed for diabetic retinopathy (DR) screening to partially address the growing screening demands associated with the increasing diabetic population worldwide, but the inaccuracy of diabetic retinopathy diagnosis remains a key issue. This project aims to enhance diagnostic accuracy by developing a novel convolutional neural network (CNN) model based on residual learning combined with self attention mechanism, leveraging deep learning. The model achieved significant performance of 81% accuracy. These results underscore the effectiveness of the model in classifying the severity of diabetic retinopathy, indicating a major advancement in diagnostic methods.