ABSTRACT
The retina, an innermost layer of the eye, comprises light-sensitive and color-sensitive cells and nerve fibers that provide vision. Retinal disorders lead to loss of vision and such diseases can cause blindness. Therefore, the diagnosis and treatment of these diseases are very important. Recently, optical coherence tomography (OCT) and fundus fluorescein angiography (FFA) imaging techniques are used to diagnose retinal disorders. In addition to these techniques, the development of deep learning-based applications for the analysis of these medical images has become a popular and active research area. In this manuscript, published articles involving the use of OCT and FFA imaging for the diagnosis of retinal diseases were comparatively examined. The comparative analysis reports that OCT imaging technique is a current and developing technology. Additionally, there has been an increase every year in studies that employ OCT with deep learning methodologies. In the continuation of the study, considering the aforementioned, studies in the literature, which made use of deep learning in the diagnosis of some important diseases utilizing OCT images, have been reviewed and summarized.
