RT info:eu-repo/semantics/article
T1 Automatic diagnosis of diabetic retinopathy from fundus images using neuro-evolutionary algorithms
A1 Aquino Brítez, Diego Ariel
A1 Ayala Gómez, Jordan
A1 Vázquez Noguera, José Luis
A1 García Torres, Miguel
A1 Mello Román, Julio César
A1 Gardel Sotomayor, Pedro Esteban
A1 Castillo Benítez, Verónica Elisa
A1 Castro Matto, Ingrid
A1 Pinto Roa, Diego Pedro
A1 Facon, Jaques
A1 Grillo, Sebastián Alberto
A2 Universidad Americana/ INCADE S.A.E
K1 Health Information Technology
K1 Public Health
AB Due to the presence of high glucose levels, diabetes mellitus (DM) is a widespread disease that can damage blood vessels in the retina and lead to loss of the visual system. To combat this disease, called Diabetic Retinopathy (DR), retinography, using images of the fundus of the retina, is the most used method for the diagnosis of Diabetic Retinopathy. The Deep Learning (DL) area achieved high performance for the classification of retinal images and even achieved almost the same human performance in diagnostic tasks. However, the performance of DL architectures is highly dependent on the optimal configuration of the hyperparameters. In this article, we propose the use of Neuroevolutionary Algorithms to optimize the hyperparameters corresponding to the DL model for the diagnosis of DR. The results obtained prove that the proposed method outperforms the results obtained by the classical approach.
PB IOS Press
SN 978-1-64368-264-8 (print)
YR 2022
FD 2022
LK http://hdl.handle.net/20.500.14066/4593
UL http://hdl.handle.net/20.500.14066/4593
LA eng
NO Address for correspondence: José Luis Vázquez Noguera, Universidad Americana, Brasilia 1100, Asunción, Paraguay; E-mail: jose.vazquez@ua.edu.py.
NO Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 03-sep-2026