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Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation.
Comput Biol Med. 2021 09; 136:104609.CB

Abstract

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 X-ray 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.

Authors+Show Affiliations

College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China. Electronic address: leiliu_v@163.com.College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China. Electronic address: zd-hy@163.com.College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China. Electronic address: yufanhua@163.com.College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China. Electronic address: aliasghar68@gmail.com.Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. Electronic address: ouyangkch@163.com.College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China. Electronic address: chenhuiling.jlu@gmail.com.Department of Computer Science, Birzeit University, POBox 14, West Bank, Palestine. Electronic address: mmafarja@birzeit.edu.Department of Information Technology, College of Computers and Information Technology, P.O. Box 11099, Taif, 21944, Taif University, Taif, Saudi Arabia. Electronic address: h.turabieh@tu.edu.sa.Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Key Laboratory of IntelligentTreatment and Life Support for Critical Diseases of Zhejiang Provincial, Wenzhou, China; Wenzhou Key Laboratory of Critical Care and Artificial Intelligence, Wenzhou, China. Electronic address: panjingye@wzhospital.cn.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

34293587

Citation

Liu, Lei, et al. "Ant Colony Optimization With Cauchy and Greedy Levy Mutations for Multilevel COVID 19 X-ray Image Segmentation." Computers in Biology and Medicine, vol. 136, 2021, p. 104609.
Liu L, Zhao D, Yu F, et al. Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation. Comput Biol Med. 2021;136:104609.
Liu, L., Zhao, D., Yu, F., Heidari, A. A., Li, C., Ouyang, J., Chen, H., Mafarja, M., Turabieh, H., & Pan, J. (2021). Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation. Computers in Biology and Medicine, 136, 104609. https://doi.org/10.1016/j.compbiomed.2021.104609
Liu L, et al. Ant Colony Optimization With Cauchy and Greedy Levy Mutations for Multilevel COVID 19 X-ray Image Segmentation. Comput Biol Med. 2021;136:104609. PubMed PMID: 34293587.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation. AU - Liu,Lei, AU - Zhao,Dong, AU - Yu,Fanhua, AU - Heidari,Ali Asghar, AU - Li,Chengye, AU - Ouyang,Jinsheng, AU - Chen,Huiling, AU - Mafarja,Majdi, AU - Turabieh,Hamza, AU - Pan,Jingye, Y1 - 2021/07/03/ PY - 2021/03/30/received PY - 2021/06/22/revised PY - 2021/06/22/accepted PY - 2021/7/23/pubmed PY - 2021/9/18/medline PY - 2021/7/22/entrez KW - Ant colony optimization KW - COVID-19 KW - Diagnosis KW - Image KW - Meta-heuristic KW - Swarm-intelligence SP - 104609 EP - 104609 JF - Computers in biology and medicine JO - Comput Biol Med VL - 136 N2 - 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 X-ray 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. SN - 1879-0534 UR - https://www.unboundmedicine.com/medline/citation/34293587/Ant_colony_optimization_with_Cauchy_and_greedy_Levy_mutations_for_multilevel_COVID_19_X-ray_image_segmentation. L2 - https://linkinghub.elsevier.com/retrieve/pii/S0010-4825(21)00403-0 DB - PRIME DP - Unbound Medicine ER -