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

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

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 (...

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Bibliographic Details
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
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Engineering
Deep learning
Optic disc
Online Access:https://hdl.handle.net/10356/179838

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