What question did this study set out to answer?

This research aims to improve the detection of diabetic retinopathy (DR) using an explainable AI framework that enhances grading accuracy.

June 17, 2026Scientific Reports1 citationsOpen Access

Explainable AI for diabetic retinopathy detection using vision transformers

MRMustafizur Rahaman MMMasrufa Akter Muni STSaima Tasnim

Key Points

This research aims to improve the detection of diabetic retinopathy (DR) using an explainable AI framework that enhances grading accuracy.
Formulated DR grading as a binary triage task (No-DR vs DR) using the All-ViT Hybrid framework.
Utilized pretrained transformer backbones, class-weighted cross-entropy, and optimizations like AdamW and OneCycle.
Preprocessed images using CLAHE and masking, with data splitting performed via stratified sampling.
Achieved an accuracy of 0.9754 and F1 score of 0.9761, with precision of 0.9658 and recall of 0.9866.
Improvement in F1 score by +1.11 percentage points and accuracy by +1.13 over the best baseline.
Produced qualitative visualizations for interpretability, suggesting potential benefits for real-world application.

Abstract

The heterogeneous acquisition, variability of orientation, and subtle lesions continue to challenge the screening of diabetic retinopathy through color fundus photographs. We formulate DR grading as a binary triage task (No-DR vs DR) and propose the All-ViT Hybrid framework, integrating complementary pretrained transformer backbones within a stability-oriented training schedule (head-only warm-up, partial unfreezing), optimized using AdamW with OneCycle scheduling, class-weighted cross-entropy, and mixed precision. Preprocessing includes luminance-space CLAHE and retinal field-of-view masking, and data splitting is performed using stratified sampling to preserve class balance. We perform post-hoc threshold tuning and test-time augmentation (optional), which is available for operating-point control and robustness. Over the competitive baselines (ConvNeXt, MobileNetV3-Large, EfficientNet-B0, DenseNet201), All-ViT Hybrid has an Accuracy of 0. 9754, F1 of 0. 9761, Precision of 0. 9658, Recall of 0. 9866, and measures of agreement of 0. 9509, MCC of 0. 9511, and Jaccard of 0. 9532. Compared to the best baseline, F1 achieves a gain of +1. 11 percentage points, an improvement in accuracy of +1. 13, and a recall improvement of +0. 81 percentage points without compromising controllable precision and these experiments were conducted on the APTOS 2019 Blindness Detection dataset. These visualizations provide qualitative interpretability and are not quantitatively validated against lesion-level annotations due to dataset limitations. These findings suggest that integrating complementary transformer representations within a unified fusion pipeline provides strong and threshold-adjustable performance for DR triage under real-world variability. The framework remains modular and extensible, with potential applicability to larger input resolutions, multi-class grading, and multi-modal clinical settings. The future research program will test external generalization and probability calibration across devices and centers.

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