Automatic classification of AMD in retinal images

Abstract

Age-related macular degeneration (AMD) is an eye disorder that may blur the clear, central vision you use for things like reading and driving. It is one of several disorders that influence the retina. The word "age-related" applies to the reality that it is more prevalent among older persons. The term "macular" refers to a region of your eye named the macula. The term "degeneration" refers to the kind of eye injury that occurs. A disease that affects older people is age-related macular degeneration (AMD). Furthermore, it is one of the primary causes of blindness or impaired vision among the elderly. In AMD, the macula produces a gradual accumulation of yellow deposits named drusen (extracellular proteins and lipids) (a part of the retina). Diagnosis with fluorescein angiography allows to identify and locate abnormal vascular processes. Most ophthalmologists now use consistent optical tomography to diagnose and evaluate follow-up in response to treatment with Avastin or Lucentis, which are injected into the eye’s vitreous at different intervals. Early detection and care, as in other eye disorders, were found to reduce the risk of blindness and vision loss. Automated retinal examination devices save patients time, resources, and vision as opposed to manual diagnosis procedures. In this case, accurate and efficient localization of Age-related Macular Degeneration (AMD) of retinal images is a critical step in diagnosing these illnesses. The purpose of this study is to suggest an automated method using the Machine Learning (CNN) method to identify patients with macular degeneration AMD using images of the ODIR dataset. A convolutional neural network (CNN) was applied to extract the deep features from the fundus images present in the data set for the classification of the images to AMD and Normal, Specificity, Sensitivity, Accuracy, F-score, and Precision metrics were used to estimate classification efficiency The highest classification accuracy was achieved as 99%.