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

• URL: http://arxiv.org/abs/2602.21943v1
• Date: Wed, 25 Feb 2026 14:26:18 GMT
• Title: Mobile-Ready Automated Triage of Diabetic Retinopathy Using Digital Fundus Images
• Authors: Aadi Joshi, Manav S. Sharma, Vijay Uttam Rathod, Ashlesha Sawant, Prajakta Musale, Asmita B. Kalamkar,
• Abstract summary: This paper presents a lightweight automated deep learning framework for efficient assessment of diabetic retinopathy severity from digital fundus images.<n>We use a MobileNetV3 architecture with a Consistent Rank Logits (CORAL) head to model the ordered progression of disease.<n>The proposed system provides a scalable and practical tool for early-stage diabetic retinopathy screening.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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