Automated detection and classification of retinal diseases using machine learning approaches

Abstract

Image processing systems are increasingly prominent in medical newlinediagnostic systems, especially in modern ophthalmology. The speedy newlinedevelopment of digital imaging and computer vision has extended the potential newlineof using these technologies in ophthalmology. An automated medical newlinediagnostic system offers standardized large-scale screening at a lower cost, newlinereduces human errors, and provides services to remote areas. newlineIn the recent past, there has been an increase in the occurrence of newlineretinal diseases. During the Covid-19 pandemic when the world was under newlinelock-down, screen time had increased multi-fold among all age groups, which newlinehas aggravated the situation. Retinal diseases such as glaucoma, Diabetic newlineRetinopathy (DR), Age-related Macular Degeneration (AMD), and many other newlinediseases that can lead to blindness, manifest themselves in the retina. Retinal newlineimages give information about the health of the visual system. Automated newlineretinal disease diagnosis systems are very useful for medical practitioners to newlinediagnose any abnormality well in advance and hence, to provide early newlinetreatment. newlineEarlier research has suggested the application of Machine Learning newline(ML) and Artificial Intelligence (AI) for automated systems for retinal image newlineanalysis. newline