COVID-19 Diagnosis Using Capsule Network and Fuzzy C-Means and Mayfly Optimization Algorithm
The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagno...
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| Published in | BioMed research international Vol. 2021; no. 1; p. 2295920 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Hindawi
2021
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2314-6133 2314-6141 2314-6141 |
| DOI | 10.1155/2021/2295920 |
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| Abstract | The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F1-score rate gives the uppermost value compared to the others. |
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| AbstractList | The COVID‐19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer‐aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID‐19. Here, a method based on fuzzy
C
‐ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X‐ray COVID‐19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state‐of‐the‐art methods, including FOMPA, MID, and 4S‐DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47%
F
1‐score rate gives the uppermost value compared to the others. The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F1-score rate gives the uppermost value compared to the others.The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F1-score rate gives the uppermost value compared to the others. The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F1-score rate gives the uppermost value compared to the others. The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy -ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% 1-score rate gives the uppermost value compared to the others. |
| Audience | Academic |
| Author | Chapnevis, Amirahmad Farki, Ali Arandian, Behdad Ghanavati, Reza Tofigh, Arash Mohammadi Salekshahrezaee, Zahra |
| AuthorAffiliation | 3 Department of General Surgery, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2 Florida Atlantic University, College of Engineering and Computer Science, Boca Raton, Florida 33431, USA 4 Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran 5 Department of Electrical Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran 6 Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran 1 Department of Information Technology Engineering, Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran |
| AuthorAffiliation_xml | – name: 2 Florida Atlantic University, College of Engineering and Computer Science, Boca Raton, Florida 33431, USA – name: 3 Department of General Surgery, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran – name: 5 Department of Electrical Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran – name: 4 Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran – name: 1 Department of Information Technology Engineering, Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran – name: 6 Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran |
| Author_xml | – sequence: 1 givenname: Ali orcidid: 0000-0003-2811-8230 surname: Farki fullname: Farki, Ali organization: Department of Information Technology EngineeringIndustrial and Systems Engineering FacultyTarbiat Modares UniversityTehranIranmodares.ac.ir – sequence: 2 givenname: Zahra surname: Salekshahrezaee fullname: Salekshahrezaee, Zahra organization: Florida Atlantic UniversityCollege of Engineering and Computer ScienceBoca RatonFlorida 33431USAfau.edu – sequence: 3 givenname: Arash Mohammadi surname: Tofigh fullname: Tofigh, Arash Mohammadi organization: Department of General SurgerySchool of MedicineShahid Beheshti University of Medical SciencesTehranIransbmu.ac.ir – sequence: 4 givenname: Reza surname: Ghanavati fullname: Ghanavati, Reza organization: Department of Chemical and Petroleum EngineeringSharif University of TechnologyTehranIransharif.ir – sequence: 5 givenname: Behdad orcidid: 0000-0002-4984-7224 surname: Arandian fullname: Arandian, Behdad organization: Department of Electrical EngineeringDolatabad BranchIslamic Azad UniversityIsfahanIranazad.ac.ir – sequence: 6 givenname: Amirahmad orcidid: 0000-0003-2258-3449 surname: Chapnevis fullname: Chapnevis, Amirahmad organization: Department of Computer Engineering and Information TechnologyAmirkabir University of TechnologyTehranIranaut.ac.ir |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34676259$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1080_13682199_2023_2173543 crossref_primary_10_1016_j_orp_2022_100265 crossref_primary_10_1007_s10462_024_10873_5 crossref_primary_10_1155_2022_6473507 crossref_primary_10_3390_su142416559 crossref_primary_10_21595_jmai_2024_23909 crossref_primary_10_1080_03772063_2023_2273305 crossref_primary_10_1155_2022_5830766 |
| Cites_doi | 10.1007/s12539-020-00403-6 10.1016/j.measurement.2019.107086 10.1371/journal.pone.0235187 10.1016/j.compmedimag.2020.101716 10.1016/j.cmpb.2020.105532 10.1016/j.cie.2020.106559 10.1038/s41598-020-71294-2 10.1007/978-3-030-59719-1_22 10.1101/2020.06.22.20137547 10.2174/1573405616666200129095242 10.1007/s40313-016-0242-6 10.1155/2021/5544742 10.1109/MITP.2020.3042379 10.1101/2020.04.13.20063461 10.1038/s41598-021-90428-8 10.1007/978-3-030-56689-0_12 10.1007/s11042-020-08699-8 10.1109/ICCS45141.2019.9065537 10.1016/j.media.2020.101794 10.1109/ACCESS.2020.3016780 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Ali Farki et al. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Ali Farki et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2021 Ali Farki et al. 2021 |
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| Snippet | The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This... The COVID‐19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This... |
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| SubjectTerms | Algorithms Computer-aided medical diagnosis Coronaviruses COVID-19 COVID-19 - diagnostic imaging Databases, Factual Datasets Diagnosis Diagnosis, Computer-Assisted - methods Epidemics Fuzzy algorithms Fuzzy logic Fuzzy systems Humans Image Enhancement Machine Learning Mathematical optimization Methods Neural networks Neural Networks, Computer Optimization Radiography - methods Sensitivity and Specificity Viral diseases X-Rays |
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| Title | COVID-19 Diagnosis Using Capsule Network and Fuzzy C-Means and Mayfly Optimization Algorithm |
| URI | https://dx.doi.org/10.1155/2021/2295920 https://www.ncbi.nlm.nih.gov/pubmed/34676259 https://www.proquest.com/docview/2589571881 https://www.proquest.com/docview/2584434495 https://pubmed.ncbi.nlm.nih.gov/PMC8526241 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/2021/2295920 |
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