A Novel Deep Learning Method for Nuclear Cataract Classification Based on Anterior Segment Optical Coherence Tomography Images

Xiaoqing Zhang, Zunjie Xiao, Risa Higashita, Wan Chen, Jin Yuan, Jiansheng Fang, Yan Hu, Jiang Liu

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

14 Citations (Scopus)

Abstract

Nuclear cataract is one of the most common types of cataract. In the recent, ophthalmologists are increasingly using anterior segment optical coherence tomography (AS-OCT) images to diagnose many ocular diseases including cataract. The relationship between cataract and the lens opacity based on AS-OCT images has been being studied in clinical pioneer research. However, using AS-OCT images to classify cataract automatically based on computer-aided diagnosis (CAD) technique has not been seriously studied. This paper proposes a novel Convolutional Neural Network (CNN) model named GraNet for nuclear cataract classification based on AS-OCT images. In the GraNet, we introduce a grading block to learn high-level feature representations based on the pointwise convolution method. To further improve the classification performance, we propose a simple and efficient cross-training method is comprised of focal loss and cross-entropy loss. Extensive experiments are conducted on the AS-OCT image dataset, the results demonstrate that the proposed methods achieve better nuclear cataract classification results than baselines.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages662-668
Number of pages7
ISBN (Electronic)9781728185262
DOIs
Publication statusPublished - 11 Oct 2020
Externally publishedYes
Event2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
Duration: 11 Oct 202014 Oct 2020

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2020-October
ISSN (Print)1062-922X

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Country/TerritoryCanada
CityToronto
Period11/10/2014/10/20

Keywords

  • AS-OCT image
  • GraNet
  • cross-training method
  • nuclear cataract

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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