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A New EYENET Model for Diagnosis of Age-Related Macular Degeneration: Diagnosis of Age-Related Macular Degeneration
Abstract
Age-related macular 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. In this chapter, to diagnose Age-related macular degeneration, the authors have proposed a new EYENET model which was obtained by combining the modified PNN and modified RBFNN and hence it poses the advantages of both the models. The amount of the disease spread in the retina can be identified by extracting the features of the retina. A total of 250 fundus images were used, out of which 150 were used for training and 100 images were used for testing. Experimental results show that PNN has an accuracy of 87%, modified PNN has an accuracy of 90% RBFNN has an accuracy of 80%, modified RBFNN has an accuracy of 85% and the proposed EYENET Model has an accuracy of 94%. This infers that the proposed EYENET model outperforms all other models.
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