Open AccessAutomated detection of early-stage ROP using a deep convolutional neural network
Author(s) -
Yo-Ping Huang,
Haobijam Basanta,
Eugene Yu Chuan Kang,
KuanJen Chen,
Yih Shiou Hwang,
ChaoLun Lai,
J. Peter Campbell,
Michael F. Chiang,
R.V. Paul Chan,
Shunji Kusaka,
Yoko Fukushima,
Wei Wu
Publication year - 2020
Publication title -
british journal of ophthalmology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.016
H-Index - 153
eISSN - 1468-2079
pISSN - 0007-1161
DOI - 10.1136/bjophthalmol-2020-316526
Subject(s) - medicine , retinopathy of prematurity , stage (stratigraphy) , receiver operating characteristic , convolutional neural network , fundus (uterus) , artificial intelligence , ophthalmology , computer science , gestational age , pregnancy , paleontology , genetics , biology
To automatically detect and classify the early stages of retinopathy of prematurity (ROP) using a deep convolutional neural network (CNN).