DETECTION OF AGE-RELATED MACULAR DEGENERATION FROM OCT IMAGES USING DOUBLE SCALE CNN ARCHITECTURE

Abstract

An ocular disease that affects the elderly is Age-related Macular Degeneration (AMD). Because of the aging population in society, AMD incidence is increasing; early diagnosis is vital to avoid vision loss in the elderly. It is a challenging process to organize a comprehensive eye screening system for detecting AMD. This paper proposes a novel Double Scale Convolutional Neural Network (DSCNN) architecture for an accurate AMD diagnosis. The architecture proposed is a DSCNN with six convolutional layers for classifying AMD or normal images. The double-scale convolution layer enables many local structures to be generated with two different filter sizes. In this proposed network, the sigmoid function is used as the classifier. The proposed CNN network is trained on the Mendeley data set and tested on four data sets, namely Mendeley, OCTID, Duke, SD-OCT Noor data set, and achieved an accuracy of 99.46%, 98.08%, 96.66%, and 94.89% respectively. The comparison with alternative methods provided results showing the efficacy of the proposed algorithm in detecting AMD. Although the proposed model is trained only on the Mendeley data set, it achieved good detection accuracy when evaluated with other data sets. This indicates the proposed model’s ability to classify AMD/Normal images from different data sets. Comparison with other approaches produced results that exhibit the efficiency of the proposed algorithm in detecting AMD. The proposed architecture can be applied in the rapid screening of the eye for the early detection of AMD. Due to less complexity and fewer learnable parameters, the proposed CNN can be implemented in real-time.