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

Zhang，Xiao Qing1,2; Hu，Yan1,2; Xiao，Zun Jie1; Fang，Jian Sheng1; Higashita，Risa1,3; Liu，Jiang1,2,4,5

2022-06-01

10.1007/s11633-022-1329-0

Machine Intelligence Research   Impact Factor & Quartile

2731-538X

2731-5398

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.

Cataract classification and grading deep learning machine learning ophthalmic image

ESCI ; EI

English

First ; Corresponding

WOS:000801156200002

20222212180845

Computer aided diagnosis ; Deep learning ; Image classification ; Learning algorithms ; Surveys

Biomedical Engineering:461.1 ; Ergonomics and Human Factors Engineering:461.4 ; Data Processing and Image Processing:723.2 ; Machine Learning:723.4.2 ; Computer Applications:723.5

2-s2.0-85130975694

Scopus

1.Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,518055,China
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Tomey Corporation,Nagoya,4510051,Japan
4.Cixi Institute of Biomedical Engineering,Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo,315300,China
5.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,518055,China

Zhang，Xiao Qing,Hu，Yan,Xiao，Zun Jie,et al. Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey[J]. Machine Intelligence Research,2022,19(3):184-208.

Zhang，Xiao Qing,Hu，Yan,Xiao，Zun Jie,Fang，Jian Sheng,Higashita，Risa,&Liu，Jiang.(2022).Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey.Machine Intelligence Research,19(3),184-208.

Zhang，Xiao Qing,et al."Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey".Machine Intelligence Research 19.3(2022):184-208.