Image-based Glaucoma Classification Using Fundus Images and Deep Learning

  • Hiram José Sandoval-Cuellar Universidad Autónoma de Querétaro
  • Gendry Alfonso-Francia Universidad Autónoma de Querétaro
  • Miguel Ángel Vázquez-Membrillo Instituto Mexicano de Oftalmología
  • Juan Manuel Ramos-Arreguín Instituto Mexicano de Oftalmología
  • Saúl Tovar Arriaga Universidad Autónoma de Querétaro
Keywords: Deep Learning, Glaucoma diagnosis, Image-base classification, Convolutional Neural Network

Abstract

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.

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Published
2021-11-21
How to Cite
Sandoval-Cuellar, H. J., Alfonso-Francia, G., Vázquez-Membrillo, M. Ángel, Ramos-Arreguín, J. M., & Tovar Arriaga, S. (2021). Image-based Glaucoma Classification Using Fundus Images and Deep Learning. Mexican Journal of Biomedical Engineering, 42(3), 28-41. Retrieved from https://rmib.mx/index.php/rmib/article/view/1188
Section
Research Articles