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Deep Transfer Learning Based Classification Model for COVID-19 Disease

The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19...

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Autores principales: Pathak, Y., Shukla, P.K., Tiwari, A., Stalin, S., Singh, S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AGBM. Published by Elsevier Masson SAS. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7238986/
https://www.ncbi.nlm.nih.gov/pubmed/32837678
http://dx.doi.org/10.1016/j.irbm.2020.05.003
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author Pathak, Y.
Shukla, P.K.
Tiwari, A.
Stalin, S.
Singh, S.
Shukla, P.K.
author_facet Pathak, Y.
Shukla, P.K.
Tiwari, A.
Stalin, S.
Singh, S.
Shukla, P.K.
author_sort Pathak, Y.
collection PubMed
description The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.
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spelling pubmed-72389862020-05-20 Deep Transfer Learning Based Classification Model for COVID-19 Disease Pathak, Y. Shukla, P.K. Tiwari, A. Stalin, S. Singh, S. Shukla, P.K. Ing Rech Biomed Original Article The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models. AGBM. Published by Elsevier Masson SAS. 2022-04 2020-05-20 /pmc/articles/PMC7238986/ /pubmed/32837678 http://dx.doi.org/10.1016/j.irbm.2020.05.003 Text en © 2020 AGBM. Published by Elsevier Masson SAS. 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 Original Article
Pathak, Y.
Shukla, P.K.
Tiwari, A.
Stalin, S.
Singh, S.
Shukla, P.K.
Deep Transfer Learning Based Classification Model for COVID-19 Disease
title Deep Transfer Learning Based Classification Model for COVID-19 Disease
title_full Deep Transfer Learning Based Classification Model for COVID-19 Disease
title_fullStr Deep Transfer Learning Based Classification Model for COVID-19 Disease
title_full_unstemmed Deep Transfer Learning Based Classification Model for COVID-19 Disease
title_short Deep Transfer Learning Based Classification Model for COVID-19 Disease
title_sort deep transfer learning based classification model for covid-19 disease
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7238986/
https://www.ncbi.nlm.nih.gov/pubmed/32837678
http://dx.doi.org/10.1016/j.irbm.2020.05.003
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