ArticleScientific reports2025
Diabetic retinopathy classification using a multi-attention residual refinement architecture.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Clinical-guided deep learning framework for diabetic retinopathy: integrating lesion-aware attention, adversarial augmentation, and uncertainty quantification.Scientific reports · 2026Article
- RDE-DR: robust deep ensemble CNNs for automated diabetic retinopathy detection from fundus images.Scientific reports · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.