Ocular Disease Severity Identification and Performance Optimisation using Custom Net Model

Abstract

Early detection and timely cure of ocular disease play a vital role to avoid irreversible vision issues in daily life. The technique fundus assessment utilizes color fundus photography, which is a very effective tool though it is expensive. Since rare symptoms of the disease are detected at the initial stage of the disease, still automated and optimized models are in urgent need for the detection of the ocular disease. Additionally, existing systems focus on image-level detection for the treatment of eyes without association employing the left and right eye information. Although they concentrate only on one or two features of the ocular disease at a time. Taking into consideration severity detection and multilabel categorization plays a vital role in ocular disease detection. So, we develop a framework to detect the disease in the early phase. And then apply the classification model for the multilabel classification of the disease. our proposed experimental result proves that the proposed Custom net model provides 99.15% of accuracy compared to the existing baseline model such as Vgg16, 19, Resnet-50 and Inception V3. The performance optimization of the proposed model is evaluated on the public datasets.