[interpretation of the paper] machine learning technology for Cataract Classification / classification

Machine Intelligence Research

Worldwide , Cataract is the main cause of visual impairment and blindness . these years , Researchers have made great progress in developing the most advanced machine learning technology for automatic Cataract Classification and classification , Aimed at early prevention of cataract , Improve the diagnostic efficiency of clinicians . The research team from Southern University of science and technology comprehensively reviewed Cataract Classification Based on ophthalmic images / The latest development of hierarchical machine learning technology . This paper summarizes the existing literature from the two directions of traditional machine learning methods and deep learning methods , And in-depth analysis of the advantages and limitations of the existing research . Besides , The article also discusses the automatic Cataract Classification Based on machine learning technology / Some challenges faced by grading technology , And put forward possible solutions for future research .

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According to the World Health Organization , Around the world 22 Billion people suffer from visual impairment . Cataracts account for about% of visual impairment 33%, It is the number one cause of blindness worldwide ( exceed 50%). Cataract patients can improve their quality of life and vision through early intervention and cataract surgery , This is to reduce the blindness rate at the same time 、 An effective method to reduce the burden of blindness caused by cataract in society .

Clinically , When proteins in the crystalline body gather , The transparency of lens area decreases , And then cause cataracts . This is related to many factors , For example, dysplasia 、 Trauma 、 Metabolic disorders 、 Genetic factors, 、 Drug induced changes 、 Age etc. . Heredity and age are the two most important factors that cause cataracts .

In the last few years , Ophthalmologists based on their experience and clinical training , Use several ophthalmic images to diagnose cataracts . This manual diagnosis mode is easy to make mistakes 、 Time consuming 、 Subjective and costly , And experienced clinicians are scarce , This gives developing countries 、 rural 、 Cataract screening and diagnosis and treatment in the community bring great challenges . In order to prevent cataract in the early stage , Improve the accuracy and efficiency of cataract diagnosis , Researchers are committed to developing computer-aided diagnosis (CAD) technology , Including traditional machine learning methods and deep learning methods , For different ophthalmic images , Realize the automatic classification of cataract / classification .

In the past decade , Deep learning has achieved great success in all fields , Including medical image analysis . It can learn low-level from raw data in an end-to-end way 、 Intermediate and advanced feature representation ( for example , Ophthalmic image ). Various deep neural network models have been used to deal with Cataract Classification / Graded tasks , For example, convolutional neural networks (CNN)、 Attention based networks 、 Fast RCNN And multi-layer perceptron (MLP) neural network .

The existing review articles summarize the types of cataract 、 Cataract Classification / Grading system and ophthalmic imaging mode ; However , So far, no article has systematically summarized the automatic Cataract Classification Based on ophthalmic imaging mode / classification ML technology . This paper summarizes systematically for the first time ML Technology for automatic Cataract Classification / The latest progress in grading , Focus on Cataract Classification / In grading ML technology , Including tradition ML Methods and deep learning methods .

This paper summarizes Web of Science、Scopus and Google Scholar Related papers in the database . Based on the collected papers 、 Summary of the research team and communication with experienced ophthalmologists , It forms the overall organizational framework of this paper ( As shown in Fig. 1 ). The research team also briefly reviewed ophthalmic imaging patterns 、 Cataract grading system and common evaluation methods , And gradually introduced ML technology , In order to provide a valuable summary for the current research , And based on ML Cataract Classification / The classification points out the potential research direction in the future .

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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

https://link.springer.com/article/10.1007/s11633-022-1329-0

https://www.mi-research.net/en/article/doi/10.1007/s11633-022-1329-0

【 The author of this article 】

Zhang Xiaoqing

Hu Yan

Xiao zunjie

Fang Jiansheng

Higashita Risa

Liu Jiang

Special thanks to the first author 、 Dr. zhangxiaoqing of South University of science and technology reviewed and modified the above contents ！

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