Retinal lesions classification for diabetic retinopathy using custom ResNet-based classifier

Abstract

Failure to diagnose and treat retinal illnesses on time might lead to irreversible blindness. The focus is on three common retinal lesions associated with diabetic retinopathy (DR): microaneurysms (MAs), haemorrhages, and exudates. The proposed solution leverages deep learning, employing a customized residual network (ResNet) based classifier trained on real-time retinal images meticulously annotated and graded by ophthalmologists. Annotation noise was a significant obstacle addressed by downsampling and augmenting the data. Compared to cutting-edge techniques, this one performs better with test-set accuracy of 93.34% across all classes. This approach holds great promise for enhancing early detection and treatment of DR by automating the recognition of these vital retinal abnormalities. The ability to automatically classify these symptoms can aid clinicians in making more precise diagnosis and starting treatments sooner. This research shows that deep learning-based approaches are highly effective, especially when combined with a customised ResNet-based classifier and thorough pre-processing steps. We observed that this method provides the ability to better the lives of patients and lower the rate of permanent blindness resulting from retinal disorders.