Generalizability of deep neural networks for vertical cup-to-disc ratio estimation in ultra-widefield and amartphone-based fundus images

• Main Authors: Yap, Boon Peng, Li, Kelvin Zhenghao, Toh, En Qi, Low, Kok Yao, Rani, Sumaya Khan, Goh, Eunice Jin Hui, Hui, Vivien Yip Cherng, Ng, Beng Koon, Lim, Tock Han
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Article
• 語言: English
• 出版: 2024
• 主題: Engineering; Deep learning; Optic disc
• 在線閱讀: https://hdl.handle.net/10356/179838
• 機構: Nanyang Technological University

Purpose: To develop and validate a deep learning system (DLS) for estimation of vertical cup-to-disc ratio (vCDR) in ultra-widefield (UWF) and smartphone-based fundus images. Methods: A DLS consisting of two sequential convolutional neural networks (CNNs) to delineate optic disc (OD) and optic cup (...