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A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection

Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in a huge number of mortalities and high morbidities worldwide. Furthermore, the symptoms-based...

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Autores principales: Khan, Habib Ullah, Khan, Sulaiman, Nazir, Shah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304977/
https://www.ncbi.nlm.nih.gov/pubmed/35874982
http://dx.doi.org/10.3389/fpubh.2022.875971
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author Khan, Habib Ullah
Khan, Sulaiman
Nazir, Shah
author_facet Khan, Habib Ullah
Khan, Sulaiman
Nazir, Shah
author_sort Khan, Habib Ullah
collection PubMed
description Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in a huge number of mortalities and high morbidities worldwide. Furthermore, the symptoms-based variations in virus type add new challenges for the research and practitioners to combat. COVID-19-infected patients comprise trenchant radiographic visual features, including dry cough, fever, dyspnea, fatigue, etc. Chest X-ray is considered a simple and non-invasive clinical adjutant that performs a key role in the identification of these ocular responses related to COVID-19 infection. Nevertheless, the defined availability of proficient radiologists to understand the X-ray images and the elusive aspects of disease radiographic replies to remnant the biggest bottlenecks in manual diagnosis. To address these issues, the proposed research study presents a hybrid deep learning model for the accurate diagnosing of Delta-type COVID-19 infection using X-ray images. This hybrid model comprises visual geometry group 16 (VGG16) and a support vector machine (SVM), where the VGG16 is accustomed to the identification process, while the SVM is used for the severity-based analysis of the infected people. An overall accuracy rate of 97.37% is recorded for the assumed model. Other performance metrics such as the area under the curve (AUC), precision, F-score, misclassification rate, and confusion matrix are used for validation and analysis purposes. Finally, the applicability of the presumed model is assimilated with other relevant techniques. The high identification rates shine the applicability of the formulated hybrid model in the targeted research domain.
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spelling pubmed-93049772022-07-23 A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection Khan, Habib Ullah Khan, Sulaiman Nazir, Shah Front Public Health Public Health Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in a huge number of mortalities and high morbidities worldwide. Furthermore, the symptoms-based variations in virus type add new challenges for the research and practitioners to combat. COVID-19-infected patients comprise trenchant radiographic visual features, including dry cough, fever, dyspnea, fatigue, etc. Chest X-ray is considered a simple and non-invasive clinical adjutant that performs a key role in the identification of these ocular responses related to COVID-19 infection. Nevertheless, the defined availability of proficient radiologists to understand the X-ray images and the elusive aspects of disease radiographic replies to remnant the biggest bottlenecks in manual diagnosis. To address these issues, the proposed research study presents a hybrid deep learning model for the accurate diagnosing of Delta-type COVID-19 infection using X-ray images. This hybrid model comprises visual geometry group 16 (VGG16) and a support vector machine (SVM), where the VGG16 is accustomed to the identification process, while the SVM is used for the severity-based analysis of the infected people. An overall accuracy rate of 97.37% is recorded for the assumed model. Other performance metrics such as the area under the curve (AUC), precision, F-score, misclassification rate, and confusion matrix are used for validation and analysis purposes. Finally, the applicability of the presumed model is assimilated with other relevant techniques. The high identification rates shine the applicability of the formulated hybrid model in the targeted research domain. Frontiers Media S.A. 2022-07-08 /pmc/articles/PMC9304977/ /pubmed/35874982 http://dx.doi.org/10.3389/fpubh.2022.875971 Text en Copyright © 2022 Khan, Khan and Nazir. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Khan, Habib Ullah
Khan, Sulaiman
Nazir, Shah
A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection
title A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection
title_full A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection
title_fullStr A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection
title_full_unstemmed A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection
title_short A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection
title_sort novel deep learning and ensemble learning mechanism for delta-type covid-19 detection
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304977/
https://www.ncbi.nlm.nih.gov/pubmed/35874982
http://dx.doi.org/10.3389/fpubh.2022.875971
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