From Pixels to Explanations: Interpretable Diabetic Retinopathy Grading with CNN-Transformer Ensembles, Visual Explainability and Vision-Language Models
The quality of diabetic retinopathy (DR) screening relies on the ability to correctly grade severity; however, many deep-learning (DL) classifiers cannot be easily interpreted in the clinical context. This study presents a methodology that combines strong discriminative models with multimodal explanations, converting retinal pixels into clinically interpretable outputs. Using the APTOS 2019 benchmark, we evaluated six representative CNN- and transformer-based backbones under a controlled protoco
Record details
Published: 25 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Transparency
Retrieved: 14 July 2026
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ethics.ai (25 April 2026), “From Pixels to Explanations: Interpretable Diabetic Retinopathy Grading with CNN-Transformer Ensembles, Visual Explainability and Vision-Language Models,” evidence record 5382, https://ethics.ai/record/5382 (originally published by arXiv).
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