Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation
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....
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| Published in | Computers in biology and medicine Vol. 136; p. 104609 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.09.2021
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0010-4825 1879-0534 1879-0534 |
| DOI | 10.1016/j.compbiomed.2021.104609 |
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| 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.
•A novel ant colony optimizer (CLACO) is proposed for COVID-19 image segmentation.•The performance of CLACO is verified by comparison with some excellent peers.•An effective COVID-19 image segmentation method based on CLACO is developed.•The segmentation method is evaluated under some representative threshold levels.•CLACO extremely improves convergence rate and image segmentation quality. |
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| AbstractList | 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. 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. •A novel ant colony optimizer (CLACO) is proposed for COVID-19 image segmentation.•The performance of CLACO is verified by comparison with some excellent peers.•An effective COVID-19 image segmentation method based on CLACO is developed.•The segmentation method is evaluated under some representative threshold levels.•CLACO extremely improves convergence rate and image segmentation quality. 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.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. AbstractThis 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. |
| ArticleNumber | 104609 |
| Author | Heidari, Ali Asghar Turabieh, Hamza Mafarja, Majdi Pan, Jingye Yu, Fanhua Zhao, Dong Chen, Huiling Li, Chengye Liu, Lei Ouyang, Jinsheng |
| Author_xml | – sequence: 1 givenname: Lei orcidid: 0000-0002-4698-2965 surname: Liu fullname: Liu, Lei email: leiliu_v@163.com organization: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China – sequence: 2 givenname: Dong surname: Zhao fullname: Zhao, Dong email: zd-hy@163.com organization: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China – sequence: 3 givenname: Fanhua surname: Yu fullname: Yu, Fanhua email: yufanhua@163.com organization: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China – sequence: 4 givenname: Ali Asghar surname: Heidari fullname: Heidari, Ali Asghar email: aliasghar68@gmail.com organization: College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China – sequence: 5 givenname: Chengye surname: Li fullname: Li, Chengye organization: Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China – sequence: 6 givenname: Jinsheng surname: Ouyang fullname: Ouyang, Jinsheng email: ouyangkch@163.com organization: Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China – sequence: 7 givenname: Huiling surname: Chen fullname: Chen, Huiling email: chenhuiling.jlu@gmail.com organization: College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China – sequence: 8 givenname: Majdi surname: Mafarja fullname: Mafarja, Majdi email: mmafarja@birzeit.edu organization: Department of Computer Science, Birzeit University, POBox 14, West Bank, Palestine – sequence: 9 givenname: Hamza surname: Turabieh fullname: Turabieh, Hamza email: h.turabieh@tu.edu.sa organization: Department of Information Technology, College of Computers and Information Technology, P.O. Box 11099, Taif, 21944, Taif University, Taif, Saudi Arabia – sequence: 10 givenname: Jingye surname: Pan fullname: Pan, Jingye email: panjingye@wzhospital.cn organization: Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34293587$$D View this record in MEDLINE/PubMed |
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| Keywords | COVID-19 Swarm-intelligence Ant colony optimization Diagnosis Meta-heuristic Image |
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| License | Copyright © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
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| Snippet | 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... AbstractThis paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic... |
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| SubjectTerms | Adaptability Algorithms Ant colony optimization Convergence Coronaviruses COVID-19 COVID-19 - diagnostic imaging Diagnosis Diagnostic systems Disease transmission Entropy Epidemics Fault diagnosis Histograms Humans Image filters Image processing Image Processing, Computer-Assisted Image segmentation Intelligence Internal Medicine Medical diagnosis Medical research Meta-heuristic Multilevel Mutation Optimization algorithms Other SARS-CoV-2 Swarm intelligence X-Rays |
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| Title | Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0010482521004030 https://www.clinicalkey.es/playcontent/1-s2.0-S0010482521004030 https://dx.doi.org/10.1016/j.compbiomed.2021.104609 https://www.ncbi.nlm.nih.gov/pubmed/34293587 https://www.proquest.com/docview/2570437838 https://www.proquest.com/docview/2555110015 https://pubmed.ncbi.nlm.nih.gov/PMC8254401 https://www.ncbi.nlm.nih.gov/pmc/articles/8254401 |
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