Explainable Deep Learning Framework for Early-Stage Diabetic Retinopathy Detection Using Attention-Guided CNNs

Abstract Diabetic retinopathy remains a leading cause of preventable blindness worldwide, and early screening is often limited by the availability of expert ophthalmologists. This paper proposes an attention-guided convolutional neural network that classifies retinal fundus images into five severity grades while producing pixel-level saliency maps that highlight microaneurysms and haemorrhages. Evaluated on a combined dataset of 18,400 fundus images drawn from public repositories, the proposed model achieves a quadratic weighted kappa of 0.91, outperforming a ResNet-50 baseline by 6.2 percentage points while remaining interpretable to clinicians through Grad-CAM overlays.

1. Introduction

Automated screening for diabetic retinopathy has been widely studied, yet most high-accuracy models operate as opaque black boxes that clinicians are reluctant to trust in high-stakes diagnostic settings. This work addresses that gap by combining a lightweight attention module with a standard convolutional backbone so that severity predictions are accompanied by a visual explanation of the regions that influenced the decision.

2. Methodology

Fundus images were preprocessed using contrast-limited adaptive histogram equalisation and resized to 512x512 pixels. An EfficientNet-B3 backbone was augmented with a convolutional block attention module (CBAM) at three stages, and the network was trained with a class-balanced focal loss over 60 epochs using five-fold cross-validation on 18,400 images spanning five DR severity grades.

3. Results

The proposed model achieved a quadratic weighted kappa of 0.91 and an area under the ROC curve of 0.97 for referable versus non-referable classification, exceeding a ResNet-50 baseline (kappa 0.857) and a vanilla EfficientNet-B3 (kappa 0.879). Grad-CAM visualisations showed close alignment between attended regions and manually annotated lesions in 88 percent of sampled cases as judged by two independent graders.

4. Conclusion

The attention-guided framework demonstrates that interpretability need not come at the cost of diagnostic accuracy. Future work will extend the approach to ultra-widefield imaging and evaluate deployment on low-cost fundus cameras in rural screening camps.

References

[1] Gulshan V. et al., Development and validation of a deep learning algorithm for detection of diabetic retinopathy, JAMA, 2016. [2] Selvaraju R. R. et al., Grad-CAM: Visual explanations from deep networks, ICCV, 2017. [3] Woo S. et al., CBAM: Convolutional block attention module, ECCV, 2018. [4] Tan M. and Le Q., EfficientNet: Rethinking model scaling for CNNs, ICML, 2019. [5] Ting D. S. W. et al., Development and validation of a deep learning system for diabetic retinopathy, JAMA, 2017.

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Received	08 Jan 2026
Accepted	19 Feb 2026
Published	28 Mar 2026