Deep Learning in Retinal Diseases Diagnosis

Abstract

Retinal diseases are among the leading causes of blindness and severe vision loss at the global level. Early diagnosis of retinal disease is of great importance in order to prevent irreversible damage to the eye. In recent years, deep learning methods have been widely used to diagnose retinal diseases. These models are developed for diagnosing a particular retinal disease. Most of these models detect and analyze disease features from the retinal image. Thanks to systems that can predict the detection of retinal diseases with high accuracy, it has allowed ophthalmologists to reduce their workload and reach more patients. This extensive literature review presents a comparative study of deep learning methods used to detect retinal diseases. For this purpose, the studies conducted on the subject between the years 2015-2022 will be examined. The related studies will be analyzed according to (1) rates of studies according to publishing years, (2) the data sets used, (3) the deep learning methods used, and (4) diagnosed retinal diseases.