Combining Multiple Deep Features for Glaucoma Classification

Annan Li, Yunhong Wang, Jun Cheng, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)

Abstract

Glaucoma is one of the leading cause of blindness. Although there is still no cure, early detection can prevent serious vision loss. Therefore automated glaucoma detection/classification is an important issue. In the past decade, segmentation based approach such as those based on cup-to-disc-ratio are popular, but single indicator limit its performance. Recently, convolutional neural network based image classification approaches that can use more image cues achieve good performance. In this paper, we propose a new glaucoma classification by combining multiple features extracted by different convolutional neural networks. Its effectiveness is clearly demonstrated on the publicly available Origa [1] dataset. It achieves an area under the receiver operating characteristic curve of 0.8483, which better than the 0.838 given by on manual marked cup-to-disc-ratio. To our knowledge, it is the first approach surpass human in glaucoma classification.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages985-989
Number of pages5
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 10 Sep 2018
Externally publishedYes
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/TerritoryCanada
CityCalgary
Period15/04/1820/04/18

Keywords

  • Convolutional neural network
  • Feature fusion
  • Glaucoma classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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