An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging
Journal:
arXiv
Published Date:
Jul 6, 2025
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that affects
millions worldwide. In the absence of effective treatment options, early
diagnosis is crucial for initiating management strategies to delay disease
onset and slow down its progression. In this study, we propose Retformer, a
novel transformer-based architecture for detecting AD using retinal imaging
modalities, leveraging the power of transformers and explainable artificial
intelligence. The Retformer model is trained on datasets of different
modalities of retinal images from patients with AD and age-matched healthy
controls, enabling it to learn complex patterns and relationships between image
features and disease diagnosis. To provide insights into the decision-making
process of our model, we employ the Gradient-weighted Class Activation Mapping
algorithm to visualize the feature importance maps, highlighting the regions of
the retinal images that contribute most significantly to the classification
outcome. These findings are compared to existing clinical studies on detecting
AD using retinal biomarkers, allowing us to identify the most important
features for AD detection in each imaging modality. The Retformer model
outperforms a variety of benchmark algorithms across different performance
metrics by margins of up to 11\.
