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arXiv · 2602.21943

Mobile-Ready Automated Triage of Diabetic Retinopathy Using Digital Fundus Images

Aadi Joshi·Manav S. Sharma·Vijay Uttam Rathod·Ashlesha Sawant·Prajakta Musale·Asmita B. Kalamkar
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Abstract

Diabetic Retinopathy (DR) is a major cause of vision impairment worldwide. However, manual diagnosis is often time-consuming and prone to errors, leading to delays in screening. This paper presents a lightweight automated deep learning framework for efficient assessment of DR severity from digital fundus images. We use a MobileNetV3 architecture with a Consistent Rank Logits (CORAL) head to model the ordered progression of disease while maintaining computational efficiency for resource-constrained environments. The model is trained and validated on a combined dataset of APTOS 2019 and IDRiD images using a preprocessing pipeline including circular cropping and illumination normalization. Extensive experiments including 3-fold cross-validation and ablation studies demonstrate strong performance. The model achieves a Quadratic Weighted Kappa (QWK) score of 0.9019 and an accuracy of 80.03 percent. Additionally, we address real-world deployment challenges through model calibration to reduce overconfidence and optimization for mobile devices. The proposed system provides a scalable and practical tool for early-stage diabetic retinopathy screening.

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Aadi Joshi, Manav S. Sharma, Vijay Uttam Rathod, Ashlesha Sawant, Prajakta Musale, Asmita B. Kalamkar. 2026-02-25. Mobile-Ready Automated Triage of Diabetic Retinopathy Using Digital Fundus Images. https://arxiv.org/abs/2602.21943

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