Diabetic retinopathy classification using a multi-attention residual refinement architecture

Diabetic Retinopathy (DR) is a complication caused by diabetes that can destroy the retina, leading to blurred vision and even blindness. We propose a multi-attention residual refinement architecture that enhances conventional CNN performance through three strategic modifications: class-specific multi-attention for diagnostic feature weighting, space-to-depth preprocessing for improved spatial information preservation, and Squeeze-and-Excitation blocks for enhanced representational capacity. Our framework demonstrates universal applicability across different CNN architectures (ResNet, DenseNet, EfficientNet, MobileNet), consistently achieving 2-5% performance improvements on the EyePACS dataset while maintaining computational efficiency. The attention mechanism provides interpretable visualizations that align with clinical pathological patterns, validating the model's diagnostic reasoning.