Explainable AI-Based Approach for Age-Related Macular Degeneration (AMD) Detection via Fundus Imaging

Abstract

Age-related macular degeneration (AMD) is a leading cause of vision loss in older people and is characterized by subtle retinal changes that make early identification difficult. Previous studies have demonstrated the efficacy of Vision Transformers (ViTs) in classifying medical images by successfully detecting retinal disorders such as AMD. This paper addresses multiple shortcomings in conventional AMD diagnostic techniques by exploring the detection and explanation of various AMD subtypes from numerical features extracted with a ViT model from fundus images through cascaded artificial intelligence (AI) models using transformers, convolutional neural networks (CNNs), and multilayer perceptrons (MLPs). The data were preprocessed to recognize intricate disease-related patterns. The best test results using the cascade method for each model type show that the MLP model achieved an accuracy of 91.86% (with a sensitivity of 92.22% and a specificity of 95.74%). The Transformer model achieved its highest accuracy of 83.72% (with a sensitivity of 83.86% and a specificity of 89.74%). The CNN model demonstrated the best performance, with an accuracy of 94.19% (with a sensitivity of 93.84% and a specificity of 96.00%). This work helps clinicians interpret AMD cases and supports decision-making revealing hidden features of AMD that are not visible to the human eye. Future research will focus on improving these systems by expanding the databases in aggregate and incorporating multimodal data.

Keywords

• Age-related macular degeneration (AMD)
• computer aided diagnosis (CAD)
• deep learning (DL)
• eXplainable artificial intelligence (XAI)