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JAIT 2023 Vol.14(6): 1186-1197
doi: 10.12720/jait.14.6.1186-1197

Deep Learning for Glaucoma Detection: R-CNN ResNet-50 and Image Segmentation

Marlene S. Puchaicela-Lozano 1, Luis Zhinin-Vera 2,3,*, Ana J. Andrade-Reyes 1, Dayanna M. Baque-Arteaga 1, Carolina Cadena-Morejón 2, Andrés Tirado-Espín 2, Lenin Ramírez-Cando 1, Diego Almeida-Galárraga 1, Jonathan Cruz-Varela 1, and Fernando Villalba Meneses 1
1. School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí, Ecuador
2. School of Mathematical and Computational Sciences, Yachay Tech University, Urcuquí, Ecuador
3. LoUISE Research Group, I3A, University of Castilla-La Mancha, Albacete, Spain
*Correspondence: luis.zhinin@uclm.es (L.Z.V.)

Manuscript received April 25, 2023; revised June 25, 2023; accepted July 11, 2023; published November 10, 2023.

Abstract—Glaucoma is a leading cause of irreversible blindness worldwide, affecting millions of people. Early diagnosis is essential to reduce visual loss, and various techniques are used for glaucoma detection. In this work, a hybrid method for glaucoma fundus image localization using pre-trained Region-based Convolutional Neural Networks (R-CNN) ResNet-50 and cup-to-disk area segmentation is proposed. The ACRIMA and ORIGA databases were used to evaluate the proposed approach. The results showed an average confidence of 0.879 for the ResNet-50 model, indicating it as a reliable alternative for glaucoma detection. Moreover, the cup-to-disc ratio was calculated using Gradient-color-based optic disc segmentation, coinciding with the ResNet-50 results in 80% of cases, having an average confidence score of 0.84. The approach suggested in this study can determine if glaucoma is present or not, with a final accuracy of 95% with specific criteria provided to guide the specialist for an accurate diagnosis. In summary, the proposed model provides a reliable and secure method for diagnosing glaucoma using fundus images.
Keywords—glaucoma, convolutional neural networks, fundus images

Cite: Marlene S. Puchaicela-Lozano, Luis Zhinin-Vera, Ana J. Andrade-Reyes, Dayanna M. Baque-Arteaga, Carolina Cadena-Morejón, Andrés Tirado-Espín, Lenin Ramírez-Cando, Diego Almeida-Galárraga, Jonathan Cruz-Varela, and Fernando Villalba Meneses, "Deep Learning for Glaucoma Detection: R-CNN ResNet-50 and Image Segmentation," Journal of Advances in Information Technology, Vol. 14, No. 6, pp. 1186-1197, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.