Mask-UnMask Regions (MUMR) Framework for Classifying AMD Grades Using Inter-Regional Interaction Analysis

Early diagnosis and effective treatment of age-related macular degeneration (AMD), a leading cause of vision impairment, are critically dependent on accurate grading. This paper presents a novel framework, named Mask-UnMask Regions (MUMR), designed to differentiate between normal retina, intermediate AMD, geographic atrophy (GA), and wet AMD using standardized retinal fundus images with an input resolution of $1024 \times 1024$ pixels. The framework initiates with the downscaling of images to a quarter of their original size via a Preserving High-Frequency Information (PHFI) module, which retains key details essential for further analysis. Additionally, we developed a simple, lightweight, yet efficient ResNet-like network for feature extraction and introduced a Region Interaction (RI) module. This module incorporates Adaptive Mask and UnMask Sub-Modules, identifying significant regions while reconstructing less relevant areas using a direction-constrained self-attention mechanism to ensure the learning of global structural cues critical for AMD grade classification. The proposed method was evaluated on a dataset comprising 864 retinal fundus images. Our model consistently outperforms state-of-the-art approaches, achieving mean accuracy, mean F1-score, and mean Cohen’s Kappa of 92.55%, 92.59%, and 89.97%, respectively. In the binary classification task of distinguishing between Non-AMD and AMD cases, the proposed approach also surpasses competing models, achieving mean accuracy, mean F1-score, and mean Cohen’s Kappa of 97.11%, 97.03%, and 94.06%, respectively. Furthermore, statistical analysis of these metrics confirms that the improvements are statistically significant, demonstrating the robustness and improved performance of our proposed framework in AMD grading.