RAW: Region Attention-Weighted Guided Network with Inter-Region Exchange for AMD Grading

This paper introduces a novel framework, termed RAW (Region Attention-Weighted Network with Inter-Region Information Exchange), specifically developed for Age-related Macular Degeneration (AMD) grading using high-resolution input images. The framework begins with a High-Fidelity Detail Retention (HFDR) block, designed to preserve critical image details essential for accurate analysis. Furthermore, a lightweight network, inspired by ResNet, is incorporated to facilitate efficient feature extraction. This is augmented by a Region Attention-Weighted (RAW) block, which highlights significant regions while mitigating interference from less relevant areas. To ensure the effective propagation of crucial information for precise AMD grading, we developed the Inter-Region Information Exchange (IRIE) block. The efficacy of the proposed RAW framework was evaluated using a dataset comprising 864 Color Retinal Fundus (CRF) images, demonstrating outstanding performance compared to existing methods. It achieved a mean accuracy of 98.08%, a mean F1-score of 98.06%, and a mean Cohen’s Kappa of 97.42%. Statistical analysis further confirms the framework’s significant advantage, particularly in terms of F1-score, underscoring its robustness and exceptional capability for AMD grading.