Deep learning for diabetic retinopathy assessments: a literature review

Diabetic retinopathy (DR) is the most important complication of diabetes. Early diagnosis by performing retinal image analysis helps avoid visual loss or blindness. A computer-aided diagnosis (CAD) system that uses images of the retinal fundus is an effective and efficient technique for the early diagnosis of diabetic retinopathy and helps specialists assess the disease. Many computer-aided diagnosis (CAD) systems have been developed to help in various stages like segmentation, detection and classification of lesions in fundus images. In the first way, the field actors have vocalized traditional machine learning (ML) techniques based on feature extraction and selection and then applied classification algorithms. The revolution of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated researchers to employ it for the diagnosis of DR and many deep learning-based methods have been introduced. In this article, we review these methods and highlight their pros and cons. We also talk about how hard it is to make deep learning methods that are good at diagnosing RD. So, our primary goal is to collaborate with experts to develop computer-aided diagnosis systems and test them in various hospital settings with varying picture quality. Finally, we highlight the remaining gaps and future research avenues to pursue.