Automated Detection of Age-Related Macular Degeneration from OCT Images Using Multipath CNN

Abstract

Age-related macular degeneration (AMD) is an eye disorder that can have harmful effects on older people. AMD affects the macula, which is the core portion of the retina. Hence, early diagnosis is necessary to prevent vision loss in the elderly. To this end, this paper proposes a novel multipath convolutional neural network (CNN) architecture for the accurate diagnosis of AMD. The architecture proposed is a multipath CNN with five convolutional layers used to classify AMD or normal images. The multipath convolution layer enables many global structures to be generated with a large filter kernel. In this proposed network, the sigmoid function is used as the classifier. The proposed CNN network is trained on the Mendeley dataset and evaluated on four datasets-the Mendeley, OCTID, Duke, and SD-OCT Noor datasets- and it achieved accuracies of 99.60%, 99.61%, 96.67%, and 93.87%, respectively. Although the proposed model is only trained on the Mendeley dataset, it achieves good detection accuracy when evaluated with other datasets. This indicates that the proposed model has the capacity to detect AMD. These results demonstrate the efficiency of the proposed algorithm in detecting AMD compared to other approaches. The proposed CNN can be applied in real-time due to its reduced complexity and learnable parameters.