Graph-Enhanced Model Improves Diabetic Retinopathy Diagnosis
Researchers (Akter et al.) publish on December 5, 2025 a graph-enhanced deep learning pipeline for diabetic retinopathy (DR) diagnosis that combines pretrained feature extractors (MobileViT, DenseNet-169) with Graph Convolutional Networks, plus quality assessment and uncertainty estimation. Evaluated on APTOS2019, Messidor-2, and EyePACS, the approach reports up to 98.45% accuracy on APTOS2019 and strong cross-dataset generalization, with code released.
Key Points
- 1Achieves up to 98.45% accuracy on APTOS2019 using MobileViT and GCN-enhanced embeddings
- 2Incorporates quality assessment and uncertainty estimation for calibrated confidence and improved clinical reliability
- 3Provides open-source code and cross-dataset validation enabling reproducible deployment in clinical fundus-imaging workflows
Scoring Rationale
Strong peer-reviewed evaluation and public code, but approach primarily combines existing models and techniques rather than introducing a radical innovation.
Sources
Public references used for this report.
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