Convolutional Neural Network for glaucoma detection in fundus images (#438)
Read ArticleDate of Conference
July 17-19, 2024
Published In
"Sustainable Engineering for a Diverse, Equitable, and Inclusive Future at the Service of Education, Research, and Industry for a Society 5.0."
Location of Conference
Costa Rica
Authors
Villegas-Cubas, Juan Elias
Capuñay-Uceda, Oscar Efraín
Celi Arévalo, Ernesto Karlo
Abstract
Glaucoma is a silent disease and is the most common cause of irreversible blindness; early detection can prevent cases of blindness and improve the patient's quality of life. This research develops a convolutional neural network (CNN) for the detection of glaucoma in fundus images. The set of LAG images was used for the training and evaluation of the proposed model. The proposed CNN consists of an input layer, seven convolutional layers, five pooling layers, one flattened layer, two fully connected layers, and a two-class output layer, which was fine-tuned with 85 epochs. The results of the evaluation of the proposed CNN with 990 images produced an accuracy of 96.57%, a sensitivity of 95.73% and a specificity of 98.83%, which represent a better performance compared to previous studies.