New machine learning method for image-based diagnosis of COVID-19
COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray...
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| Published in | PloS one Vol. 15; no. 6; p. e0235187 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
26.06.2020
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0235187 |
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| Abstract | COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively. |
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| AbstractList | COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively. COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively. |
| Audience | Academic |
| Author | Sahlol, Ahmed T. Elaziz, Mohamed Abd Hosny, Khalid M. Darwish, Mohamed M. Salah, Ahmad Lu, Songfeng |
| AuthorAffiliation | 3 Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt 5 Faculty of Specific Education, Damietta University, Damietta, Egypt 4 Faculty of Science, Assiut University, Assiut, Egypt 1 Faculty of Science, Zagazig University, Zagazig, Egypt 2 School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China Politechnika Slaska, POLAND |
| AuthorAffiliation_xml | – name: 1 Faculty of Science, Zagazig University, Zagazig, Egypt – name: 5 Faculty of Specific Education, Damietta University, Damietta, Egypt – name: 4 Faculty of Science, Assiut University, Assiut, Egypt – name: Politechnika Slaska, POLAND – name: 2 School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – name: 3 Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt |
| Author_xml | – sequence: 1 givenname: Mohamed Abd surname: Elaziz fullname: Elaziz, Mohamed Abd – sequence: 2 givenname: Khalid M. orcidid: 0000-0001-8065-8977 surname: Hosny fullname: Hosny, Khalid M. – sequence: 3 givenname: Ahmad surname: Salah fullname: Salah, Ahmad – sequence: 4 givenname: Mohamed M. surname: Darwish fullname: Darwish, Mohamed M. – sequence: 5 givenname: Songfeng surname: Lu fullname: Lu, Songfeng – sequence: 6 givenname: Ahmed T. surname: Sahlol fullname: Sahlol, Ahmed T. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32589673$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2020 Public Library of Science 2020 Elaziz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Elaziz et al 2020 Elaziz et al |
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| Title | New machine learning method for image-based diagnosis of COVID-19 |
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