A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.

Authors

José Araque-Gallardo, Eugenia Arrieta-Rodríguez, Lenis Rueda-Gómez, Oscar Teherán-Forero, Emiro De-La-Hoz-Franco, Margarita Gamarra, Javier Sierra-Carrillo, José Escorcia-Gutierrez

Published in

iScience. Volume 29. Issue 7. Pages 116565. Jul 17, 2026. Epub Jun 24, 2026.

Abstract

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide, particularly in low- and middle-income countries. Although expert analysis of color fundus images (CFIs) enables reliable classification, this process is time-consuming. In recent years, deep learning (DL) techniques have demonstrated remarkable performance in analyzing CFI for the detection (binary classification) and multi-class classification of DR. To synthesize recent advances in this field, this study presents a systematic review, analyzing 146 peer-reviewed studies published between 2019 and 2025. This review examines lesion detection and segmentation, as well as DR detection and classification in CFI using two approaches: direct full-image and lesion-based classification. In addition, a brief scientometric analysis was performed to identify publication trends, leading journals, and countries contributing to this domain. The insights provided by this review aim to support researchers in selecting effective strategies and advancing the development of DL-based systems for the detection and classification of DR.

PMID:
42491638
Bibliographic data and abstract were imported from PubMed on 24 Jul 2026.

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