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

AI / informaticsiScience· Jul 2026

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

José Araque-Gallardo, Eugenia Arrieta-Rodríguez, Lenis Rueda-Gómez … José Escorcia-Gutierrez

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Review analyzed 146 studies of deep learning for diabetic retinopathy classification.

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

José Araque-Gallardo … José Escorcia-Gutierrez

iScience · Jul 2026

Background

Diabetic retinopathy (diabetic retinopathy) is the leading cause of preventable blindness worldwide, particularly in low- and middle-income countries.

Purpose

The insights provided by this review aim to support researchers in selecting effective strategies and advancing the development of deep learning-based systems for the detection and classification of diabetic retinopathy.

Methods

To synthesize recent advances in this field, this study presents a systematic review, analyzing 146 peer-reviewed studies published between 2019 and 2025.

n = 146 peer-reviewed studies

146

Results

146

studies reviewed deep learning tools that identify diabetic eye disease from retinal photographs

n = 146 peer-reviewed studies

More results

Although expert analysis of color fundus images (color fundus images) enables reliable classification, this process is time-consuming.

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Conclusion

The insights provided by this review aim to support researchers in selecting effective strategies and advancing the development of deep learning-based systems for the detection and classification of diabetic retinopathy.

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

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

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