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Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in infected individuals. The more severe cases may lead to death. Automated methods which can detect COVID-19 in radiological images can he...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277546/ https://www.ncbi.nlm.nih.gov/pubmed/34276262 http://dx.doi.org/10.1016/j.asoc.2021.107698 |
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author | Bandyopadhyay, Rajarshi Basu, Arpan Cuevas, Erik Sarkar, Ram |
author_facet | Bandyopadhyay, Rajarshi Basu, Arpan Cuevas, Erik Sarkar, Ram |
author_sort | Bandyopadhyay, Rajarshi |
collection | PubMed |
description | Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in infected individuals. The more severe cases may lead to death. Automated methods which can detect COVID-19 in radiological images can help in the screening of patients. In this work, a two-stage pipeline composed of feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images is proposed. For feature extraction, a state-of-the-art Convolutional Neural Network (CNN) model based on the DenseNet architecture is utilised. To eliminate the non-informative and redundant features, the meta-heuristic called Harris Hawks optimisation (HHO) algorithm combined with Simulated Annealing (SA) and Chaotic initialisation is employed. The proposed approach is evaluated on the SARS-COV-2 CT-Scan dataset which consists of 2482 CT-scans. Without the Chaotic initialisation and the SA, the method gives an accuracy of around 98.42% which further increases to 98.85% on the inclusion of the two and thus delivers better performance than many state-of-the-art methods and various meta-heuristic based FS algorithms. Also, comparison has been drawn with many hybrid variants of meta-heuristic algorithms. Although HHO falls behind a few of the hybrid variants, when Chaotic initialisation and SA are incorporated into it, the proposed algorithm performs better than any other algorithm with which comparison has been drawn. The proposed algorithm decreases the number of features selected by around 75% , which is better than most of the other algorithms. |
format | Online Article Text |
id | pubmed-8277546 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82775462021-07-14 Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans Bandyopadhyay, Rajarshi Basu, Arpan Cuevas, Erik Sarkar, Ram Appl Soft Comput Article Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in infected individuals. The more severe cases may lead to death. Automated methods which can detect COVID-19 in radiological images can help in the screening of patients. In this work, a two-stage pipeline composed of feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images is proposed. For feature extraction, a state-of-the-art Convolutional Neural Network (CNN) model based on the DenseNet architecture is utilised. To eliminate the non-informative and redundant features, the meta-heuristic called Harris Hawks optimisation (HHO) algorithm combined with Simulated Annealing (SA) and Chaotic initialisation is employed. The proposed approach is evaluated on the SARS-COV-2 CT-Scan dataset which consists of 2482 CT-scans. Without the Chaotic initialisation and the SA, the method gives an accuracy of around 98.42% which further increases to 98.85% on the inclusion of the two and thus delivers better performance than many state-of-the-art methods and various meta-heuristic based FS algorithms. Also, comparison has been drawn with many hybrid variants of meta-heuristic algorithms. Although HHO falls behind a few of the hybrid variants, when Chaotic initialisation and SA are incorporated into it, the proposed algorithm performs better than any other algorithm with which comparison has been drawn. The proposed algorithm decreases the number of features selected by around 75% , which is better than most of the other algorithms. Elsevier B.V. 2021-11 2021-07-14 /pmc/articles/PMC8277546/ /pubmed/34276262 http://dx.doi.org/10.1016/j.asoc.2021.107698 Text en © 2021 Elsevier B.V. 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. |
spellingShingle | Article Bandyopadhyay, Rajarshi Basu, Arpan Cuevas, Erik Sarkar, Ram Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans |
title | Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans |
title_full | Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans |
title_fullStr | Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans |
title_full_unstemmed | Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans |
title_short | Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans |
title_sort | harris hawks optimisation with simulated annealing as a deep feature selection method for screening of covid-19 ct-scans |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277546/ https://www.ncbi.nlm.nih.gov/pubmed/34276262 http://dx.doi.org/10.1016/j.asoc.2021.107698 |
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