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

arXiv: https://arxiv.org/abs/2602.21943 · HTML: https://mlsanity.com/paper/2602.21943/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [AI-Driven Diabetic Retinopathy Diagnosis Enhancement through Image Processing and Salp Swarm Algorithm-Optimized Ensemble Network](https://mlsanity.com/paper/2503.14209/) (2025)
- [Convolutional Neural Network Model for Diabetic Retinopathy Feature Extraction and Classification](https://mlsanity.com/paper/2310.10806/) (2023)

## Abstract (arXiv)

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.