Selected review | machine learning technology for Cataract Classification / classification

【MIR Reading guide 】 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 .

 

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 .

 

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 】

 

 

About Machine Intelligence Research

 

Machine Intelligence Research（ abbreviation MIR, Original title International Journal of Automation and Computing） Sponsored by the Institute of automation, Chinese Academy of Sciences , On 2022 It was officially published in .MIR Based on the domestic 、 Global oriented , Focus on serving the national strategic needs , Publish the latest original research papers in the field of machine intelligence 、 review 、 Comments, etc , Comprehensively report the basic theories and cutting-edge innovative research achievements in the field of international machine intelligence , Promote international academic exchanges and discipline development , Serve the progress of national artificial intelligence science and technology . The journal was selected " China Science and technology journal excellence action plan ", Has been ESCI、EI、Scopus、 The core journals of science and technology in China 、CSCD Wait for the database to include .

 

MIR Good articles in the past

▼

Fudan qiuxipeng team | review ： Paradigm shift in naturallanguageprocessing

Latest good article | Interpretable counterdefense based on causal inference

A collection of high quality reviews | Covers evolutionary computing 、 Deep audio-visual learning 、 Target tracking ......

A collection of high-quality solicitations | Covering Palmprint and palmar vein recognition 、 Gesture recognition 、 Self supervised learning .......

He Huiguang team, Institute of automation, Chinese Academy of Sciences | Based on the RGEC The new network of

lenovo CTO Ruiyong team | Knowledge mining ： Cross domain overview

Editor in chief academician Tan Tieniu sent a message , MIR The first issue was officially published ！

Zhan Zhihui team of South China University of Technology | review : Evolutionary computing for expensive optimization

Yin Xucheng team of Beijing University of science and Technology | Small sample image classification based on weak correlation knowledge integration

Zhang MINLING team of Southeast University | Multidimensional classification method based on selective feature augmentation

 

 

The database contains information

▼

Good news | MIR By ESCI Included ！

Good news | MIR By EI And Scopus Database Collection

The good news of the new year ！MIR Included in the “ The core journals of science and technology in China ”