Honggu Kang, Huigyu Yang, Moonseong Kim, Hyunseung Choo, Journal of Internet Computing and Services, Vol. 25, No. 3, pp. 19-25, Jun. 2024
10.7472/jksii.2024.25.3.19, Full Text:  HTML
Keywords: Ocular disease diagnosis and classification, CFI, CNN, Deep Learning, Artificial intelligence

Abstract

The worldwide aging population trend is causing an increase in the incidence of major retinal diseases that can lead to blindness, including glaucoma, cataract, and macular degeneration. In the field of ophthalmology, there is a focused interest in diagnosing diseases that are difficult to prevent in order to reduce the rate of blindness. This study proposes a deep learning approach to accurately diagnose ocular diseases in fundus photographs using less data than traditional methods. For this, Convolutional Neural Network (CNN) models capable of effective learning with limited data were selected to classify Conventional Fundus Images (CFI) from various ocular disease patients. The chosen CNN models demonstrated exceptional performance, achieving high Accuracy, Precision, Recall, and F1-score values. This approach reduces manual analysis by ophthalmologists, shortens consultation times, and provides consistent diagnostic results, making it an efficient and accurate diagnostic tool in the medical field.

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