Explainable Diabetic Retinopathy Classification Using Vision Foundation Models
Diabetic retinopathy (DR) is a major cause of preventable blindness, creating a need for accurate and trustworthy automated screening. This study investigates an explainable DR classification framework using vision foundation models and multiple transfer learning strategies. Three backbones, DINOv2, CLIP, and Vision Transformer (ViT), were evaluated using full fine-tuning, linear probing, and Low-Rank Adaptation (LoRA). Models were trained and internally evaluated on the ODIR dataset and externally evaluated on APTOS to assess generalization. DINOv2-LoRA achieved the highest internal AUROC of 0.758, while DINOv2 full fine-tuning and ViT full fine-tuning achieved the highest external AUROC...
| Authors | Abhishek Verma, Anila Krishna, Abhishek Gajanan Bankar et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 August 2026 |
| Last updated at source | 4 September 2026 |
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