Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey

Xiao Qing Zhang, Yan Hu, Zun Jie Xiao, Jian Sheng Fang, Risa Higashita, Jiang Liu

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

Cataracts are the leading cause of visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading, aiming to prevent cataracts early and improve clinicians’ diagnosis efficiency. This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning methods and deep learning methods. This survey also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.

Original languageEnglish
Pages (from-to)184-208
Number of pages25
JournalMachine Intelligence Research
Volume19
Issue number3
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

Keywords

  • Cataract
  • classification and grading
  • deep learning
  • machine learning
  • ophthalmic image

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Modelling and Simulation
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence
  • Applied Mathematics

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