Computer Vision and Deep Learning for Automated Detection of Diabetic Retinopathy in Low-Resource Settings

Abstract

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide, disproportionately affecting people in low- and middle-income countries (LMICs) where access to ophthalmic specialists and screening infrastructure is limited. Advances in computer vision and deep learning have produced automated systems that detect referable DR from retinal fundus images with accuracy approaching expert graders. This article provides a comprehensive, submission-ready review and methods paper that (1) synthesizes the state of the art in deep learning for DR detection; (2) presents rigorous methodology for model development, evaluation, and deployment in low-resource settings; (3) addresses data, algorithmic, clinical validation, explainability, and regulatory considerations; and (4) proposes operational pathways for scalable, equitable screening programs using affordable fundus photography and edge computing. We integrate theory and practical guidance dataset curation, image preprocessing, model architectures (CNNs, attention and Transformer hybrids), transfer learning, handling class imbalance, uncertainty quantification, and interpretability to form a blueprint for researchers and implementers aiming to deploy safe, effective DR screening at scale. Key references from peer-reviewed literature and policy guidance back our recommendations