A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection
<p dir="ltr">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. Fu...
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2022
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| _version_ | 1864513548232687616 |
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| author | Habib Ullah Khan (12024579) |
| author2 | Sulaiman Khan (12585349) Shah Nazir (14779162) |
| author2_role | author author |
| author_facet | Habib Ullah Khan (12024579) Sulaiman Khan (12585349) Shah Nazir (14779162) |
| author_role | author |
| dc.creator.none.fl_str_mv | Habib Ullah Khan (12024579) Sulaiman Khan (12585349) Shah Nazir (14779162) |
| dc.date.none.fl_str_mv | 2022-07-08T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fpubh.2022.875971 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Novel_Deep_Learning_and_Ensemble_Learning_Mechanism_for_Delta-Type_COVID-19_Detection/29069684 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence hybrid deep learning Delta-type COVID-19 VGG16 ensemble learning technique AI |
| dc.title.none.fl_str_mv | A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Public Health<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fpubh.2022.875971" target="_blank">https://dx.doi.org/10.3389/fpubh.2022.875971</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_91c2250a13149562a398dba9b47fbee1 |
| identifier_str_mv | 10.3389/fpubh.2022.875971 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29069684 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 DetectionHabib Ullah Khan (12024579)Sulaiman Khan (12585349)Shah Nazir (14779162)EngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligencehybrid deep learningDelta-type COVID-19VGG16ensemble learning techniqueAI<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Public Health<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fpubh.2022.875971" target="_blank">https://dx.doi.org/10.3389/fpubh.2022.875971</a></p>2022-07-08T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fpubh.2022.875971https://figshare.com/articles/journal_contribution/A_Novel_Deep_Learning_and_Ensemble_Learning_Mechanism_for_Delta-Type_COVID-19_Detection/29069684CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290696842022-07-08T03:00:00Z |
| spellingShingle | A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection Habib Ullah Khan (12024579) Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence hybrid deep learning Delta-type COVID-19 VGG16 ensemble learning technique AI |
| status_str | publishedVersion |
| 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 | A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection |
| topic | Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence hybrid deep learning Delta-type COVID-19 VGG16 ensemble learning technique AI |