SciELO - Scientific Electronic Library Online

vol.42 número3Identificación de 7 Movimientos de la Mano Humana Utilizando sEMG - 360° en el Antebrazo índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados




Links relacionados

  • No hay artículos similaresSimilares en SciELO


Revista mexicana de ingeniería biomédica

versión On-line ISSN 2395-9126versión impresa ISSN 0188-9532


SANDOVAL-CUELLAR, H. J. et al. Image-based Glaucoma Classification Using Fundus Images and Deep Learning. Rev. mex. ing. bioméd [online]. 2021, vol.42, n.3, 1188.  Epub 22-Mar-2022. ISSN 2395-9126.

Glaucoma is an eye disease that gradually affects the optic nerve. Intravascular high pressure can be controlled to prevent total vision loss, but early glaucoma detection is crucial. The optic disc has been a notable landmark for finding abnormalities in the retina. The rapid development of computer vision techniques has made it possible to analyze eye conditions from images enabling to help a specialist to make a diagnosis using a technique that is non-invasive in its initial stage through fundus images. We propose a methodology glaucoma detection using deep learning. A convolutional neural network (CNN) is trained to extract multiple features, to classify fundus images. The accuracy, sensitivity, and the area under the curve obtained using the ORIGA database are 93.22%, 94.14%, and 93.98%. The use of the algorithm for the automatic region of interest detection in conjunction with our CNN structure considerably increases the glaucoma detecting accuracy in the ORIGA database.

Palabras llave : Deep Learning; Glaucoma diagnosis; Image-based classification; Convolutional Neural Network.

        · resumen en Español     · texto en Inglés