Enhancing Fundus Image Quality for Improved Age-Related Macular Degeneration Detection Using Deep Learning

Abstract

Age-related Macular Degeneration (AMD) is a leading cause of vision loss in the elderly, where early detection plays a critical role in slowing disease progression. Deep learning approaches have shown strong potential for automated AMD diagnosis from retinal images; however, their performance can be hindered by image quality variations, illumination inconsistencies, and artifacts. In this study, we propose a custom contrast enhancement mask to improve lesion visibility in fundus images prior to classification. Using the publicly available ADAM challenge dataset, three deep learning architectures-YOLOv8n-cls, InceptionV3, and a modified U-Net encoder-were trained and evaluated on both the original and enhanced datasets. Experimental results demonstrate that the enhancement method substantially improves classification performance across all models, with YOLOv8n-cls achieving the highest accuracy with 91.70%, and specificity with 96.76%. These findings highlight the importance of preprocessing in medical image analysis and suggest that lightweight models, when combined with effective enhancement techniques, can achieve high accuracy suitable for clinical deployment.