Screening Retinal Images and Extraction of the Retinal Blood Vessel for Identifying Diseases and Classification of Arteries and Veins by Using Deep Learning

Abstract

Abstract: In recent years, the extraction of retinal blood vessels from low contrast retinal images has become a challenging task for diagnosing retinal diseases such as Diabetic Retinopathy, Agerelated Macular Degeneration (AMD), Retinopathy of Prematurity (ROP), cataract, and glaucoma. Another challenge is screening the retinal image to identify the disease early on. However, data analysis from a large population-based study of retinal diseases is required to help resolve the uncertainty in identifying the retinal disease based on retinal image classification using deep learning approaches from the retinal diseases dataset. Therefore, we proposed the survey on the deep learning approach for screening the retinal image to identify the early stages of the disease and discussed retinal disease analysis based on deep learning approaches to detect Diabetic Retinopathy, AMD ROP, and Glaucoma. We also discuss deep learning applications in the segmentation of retinal blood vessels, extraction of the optic disc, optic cup, and fovea, and OCT segmentation to detect retinal disease for diagnosis of diseases. Finally, discuss the classification of arteries/veins using a deep learning approach.