Glaucoma Detection Using Transfer Learning with the Faster R-CNN Model and a ResNet-50-FPN Backbone

Noirane Getirana de Sá, Daniel Dantas, Gilton Ferreira da Silva

2024

Abstract

Early detection of glaucoma has the potential to prevent vision loss. The application of artificial intelligence can enhance the cost-effectiveness of glaucoma detection by reducing the need for manual intervention. Glaucoma is the second leading cause of blindness and, due to its asymptomatic nature until advanced stages, diagnosis is often delayed. Having a general understanding of the disease’s pathophysiology, diagnosis, and treatment can assist primary care physicians in referring high-risk patients for comprehensive ophthalmo-logic examinations and actively participating in the care of individuals affected by this condition. This article describes a method for glaucoma detection with the Faster R-CNN model and a ResNet-50-FPN backbone. Our experiments demonstrated greater accuracy compared to models such as, AlexNet, VGG-11, VGG-16, VGG-19, GoogleNet-V1, ResNet-18, ResNet-50, ResNet-101 and ResNet-152.

Paper Citation

in Harvard Style

Getirana de Sá N., Dantas D. and Ferreira da Silva G. (2024). Glaucoma Detection Using Transfer Learning with the Faster R-CNN Model and a ResNet-50-FPN Backbone. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 837-844. DOI: 10.5220/0012706500003690

in Bibtex Style

@conference{iceis24,
author={Noirane Getirana de Sá and Daniel Dantas and Gilton Ferreira da Silva},
title={Glaucoma Detection Using Transfer Learning with the Faster R-CNN Model and a ResNet-50-FPN Backbone},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={837-844},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012706500003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Glaucoma Detection Using Transfer Learning with the Faster R-CNN Model and a ResNet-50-FPN Backbone
SN - 978-989-758-692-7
AU - Getirana de Sá N.
AU - Dantas D.
AU - Ferreira da Silva G.
PY - 2024
SP - 837
EP - 844
DO - 10.5220/0012706500003690
PB - SciTePress