AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH026080019
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ORIGINAL RESEARCH ARTICLE

Particle swarm optimization design of convolutional neural networks with a fuzzy learning rate estimation for diabetic retinopathy detection and classification

Daniela Sánchez1 ,  Patricia Melin1 ,  Rodrigo Cordero-Martínez1 ,  Oscar Castillo1*
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1 Division of Graduate Studies and Research, Tijuana Institute of Technology, National Technological Institute of Mexico, Tijuana, Baja California, Mexico
AIH, 026080019 https://doi.org/10.36922/AIH026080019
Received: 18 February 2026 | Revised: 31 March 2026 | Accepted: 2 April 2026 | Published online: 24 April 2026
© 2026 by the Author(s. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

One of the leading causes of blindness in young adults is diabetic retinopathy, a chronic eye complication that arises from diabetes mellitus. It occurs due to progressive damage caused by persistently high blood glucose levels. Convolutional neural networks are becoming a fundamental tool for analyzing and classifying medical images, making them a valuable aid in medical diagnosis. This work proposes a particle swarm optimization-based design of convolutional neural networks. During training, fuzzy logic estimates the learning rate based on the accuracy and loss of both the training and validation sets. The Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection dataset was used to test the proposed method. In the binary case, the best accuracy was 96.73%; in the multiclass case, it was 77.49%. The results demonstrate that the proposed method, when fuzzy logic is applied, improves diagnostic accuracy for diabetic retinopathy compared with a design that excludes fuzzy logic from the learning process and applies the adaptive moment estimation algorithm.

Graphical abstract
Keywords
Particle swarm optimization
Convolutional neural networks
Swarm intelligence
Learning rate
Type-1 fuzzy logic
Adam algorithm
Diabetic retinopathy
Funding
None.
Conflict of interest
Oscar Castillo is an Editorial Board Member of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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