Mobile-Ready Automated Triage of Diabetic Retinopathy Using Digital Fundus Images
This provides a scalable tool for early-stage diabetic retinopathy screening, addressing delays in manual diagnosis, though it is incremental as it builds on existing methods like MobileNetV3.
The paper tackles the problem of automated diabetic retinopathy severity assessment from fundus images by presenting a lightweight deep learning framework, achieving a Quadratic Weighted Kappa score of 0.9019 and 80.03% accuracy.
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.