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

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. A research team of Southern University of Science and Technology provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. The survey summarizes 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, it discusses several challenges of automatic cataract classification/grading based on machine learning techniques and presents possible solutions to these challenges for future research.

 

 

According to the World Health Organization (WHO), it is estimated that approximately 2.2 billion people suffer from visual impairment. Cataracts account for about 33% of visual impairment and are the number one cause of blindness (more than 50%) worldwide. Cataract patients can improve life quality and vision through early intervention and cataract surgery, which are efficient methods to reduce the blindness ratio and cataract-blindness burden for society simultaneously.

 

Clinically, cataracts are the transparency loss of crystalline lens area, which occurs when the protein inside the lens clumps together[3]. They are associated with many factors[4], such as developmental abnormalities, trauma, metabolic disorders, genetics, drug-induced changes, age, etc. Genetics and ageing are two of the most important factors for cataracts. 

 

Over the past years, ophthalmologists have used several ophthalmic images to diagnose cataracts based on their experience and clinical training. This manual diagnosis mode is error-prone, time-consuming, subjective, and costly, which is a great challenge in developing countries or rural communities, where experienced clinicians are scarce. To prevent cataracts early and improve the precision and efficiency of cataract diagnosis, researchers have made great efforts in developing computer-aided diagnosis (CAD) techniques for automatic cataract classification/grading on different ophthalmic images, including conventional machine learning methods and deep learning methods.

 

Over the past ten years, deep learning has achieved great success in various fields, including medical image analysis, which can be viewed as a representation learning approach. It can learn low-level, middle-level, and high-level feature representations from raw data in an end-to-end manner (e.g., ophthalmic images). Various deep neural networks have been utilized to tackle cataract classification/grading tasks like convolutional neural networks (CNNs), attention-based networks, Faster-RCNN, and multilayer perceptron (MLP) neural networks. 

 

Previous surveys had summarized cataract types, cataract classification/grading systems, and ophthalmic imaging modalities, respectively; however, none had summarized ML techniques based on ophthalmic imaging modalities for automatic cataract classification/grading systematically. This is the first survey that systematically summarizes recent advances in ML techniques for automatic cataract classification/grading. This survey mainly focuses on surveying ML techniques in cataract classification/grading, comprised of conventional ML methods and deep learning methods.

 

This paper surveyed these published papers through Web of Science (WoS), Scopus, and Google Scholar databases. A general organization framework is provided for this survey according to collected papers, the summary of the research team, and discussion with experienced ophthalmologists (Figure 1). The research team also reviews ophthalmic imaging modalities, cataract grading systems, and commonly-used evaluation measures in brief, and introduces ML techniques step by step, with the hope of providing a valuable summary of current research and presenting potential research directions for ML-based cataract classification/grading 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

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Release Date: 2022-07-04 Visited: