Automated detection of early-stage ROP using a deep convolutional neural network.

Journal: The British journal of ophthalmology
PMID:

Abstract

BACKGROUND/AIM: To automatically detect and classify the early stages of retinopathy of prematurity (ROP) using a deep convolutional neural network (CNN).

Authors

  • Yo-Ping Huang
    National Taipei University of Technology, Taipei City, Taiwan.
  • Haobijam Basanta
    Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan.
  • Eugene Yu-Chuan Kang
    Department of Ophthalmology, Chang Gung Memorial Hospital and College of Medicine, Chang Gung University, Taoyuan, Taiwan.
  • Kuan-Jen Chen
    Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, Taiwan, 333.
  • Yih-Shiou Hwang
    Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, Taiwan, 333.
  • Chi-Chun Lai
    Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, Taiwan, 333.
  • John P Campbell
    Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA.
  • Michael F Chiang
    National Eye Institute, National Institutes of Health, Bethesda, Maryland.
  • Robison Vernon Paul Chan
    Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, Chicago, Illinois, USA.
  • Shunji Kusaka
  • Yoko Fukushima
    Department of Ophthalmology, Osaka University, Osaka, Japan.
  • Wei-Chi Wu
    Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, Taiwan, 333. weichi666@gmail.com.