Automated diagnosis of age-related macular degeneration using machine learning techniques

Age-related macular (ARM) degeneration is an eye disease, that gradually degrades the macula, a part of the retina, which is responsible for central vision. It occurs in one of the two types, dry and wet age-related macular degeneration. The purpose of this paper is to diagnose the retinal disease age-related macular degeneration. An automated approach is proposed to help in the early detection of age-related macular degeneration using three models and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. Detection of age-related macular degeneration disease has been done using probabilistic neural network (PNN), Bayesian classification and support vector machine (SVM) and the two types of age-related macular degeneration are classified and diagnosed successfully. The results show that SVM achieves a higher performance measure than probabilistic neural network and Bayes classification.