Authors:
Noirane Getirana de Sá
;
Daniel Dantas
and
Gilton Ferreira da Silva
Affiliation:
Departamento de Computação, Universidade Federal de Sergipe, São Cristóvão, SE, Brazil
Keyword(s):
Ophtalmology, Diagnosis, Machine Learning, Deep Learning, Region-Based.
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.