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

I. 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.

II. 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.

III. RESULTS AND EVALUATION

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.

IV. 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.

V. REFERENCES