A comprehensive review of retinal disease diagnosis and open access datasets: Fundus and OCT images

Abstract

The rapid advancements in deep learning algorithms and the availability of large, open-access databases of fundus and OCT (optical coherence tomography) images have contributed greatly to advancements in computer-assisted diagnostics and the localization of various disorders affecting the retina. This study offers a comprehensive examination of retinal diseases and various recent applications of deep learning strategies for categorising key retinal conditions, such as diabetic retinopathy, glaucoma, age-related macular degeneration, choroidal neovascularization, retinal detachment, media haze, myopia, and dry eyes. Open-access datasets continue to play a critical role in the advancement of digital health research and innovation within the field of ophthalmology. Thirty open-access databases containing fundus and OCT (optical coherence tomography) pictures, which are often utilised by researchers, were carefully examined in this work. A summary of these datasets was created, which includes the number of images, dataset size, and supplementary items in the dataset, as well as information on eye disease and country of origin. We also discussed challenges and limitations of novel deep learning models. Finally, in conclusion, we discussed some important insights and provided directions for future research opportunities.