期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 136, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2021.104609
关键词
Ant colony optimization; Diagnosis; Image; Meta-heuristic; COVID-19; Swarm-intelligence
类别
资金
- National Natural Science Foundation of China [62076185, U1809209]
- Guangxi Key Laboratory of Trusted Software [KX202049]
- Guangxi science and technology base and talent project [20325004 CE]
- Thirteenth Five-Year Science and Technology Project of Jilin Provincial Department of Education [JJKH20200829KJ]
- Changchun Normal University [BS [2020]]
- Taif University Researchers Sup-porting Project, Taif University, Taif, Saudi Arabia [TURSP-2020/125]
- Wenzhou Key Technology Breakthrough Program on Prevention and Treatment for COVID-19 Epidemic [ZG2020012]
- Wenzhou University Application Technology Collaborative Innovation Center, Smart Medical Education Collaborative Innovation Center
This study introduces a multilevel COVID-19 X-ray image segmentation method based on ant colony optimization. By improving the algorithm, it effectively enhances the diagnostic level.
This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a new ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was successfully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 Xray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com.
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