D-Cov19Net: A DNN based COVID-19 detection system using lung sound

The limitations of proper detectors for COVID-19 for the proliferating number of patients provoked us to build an auto-diagnosis system to detect COVID-19 infection using only one parameter. Our designed model is based on Deep Convolution Neural Network and considers lung/respiratory sound as the de...

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Detalles Bibliográficos
Autores principales: Chatterjee, Sukanya, Roychowdhury, Jishnu, Dey, Anilesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753453/
https://www.ncbi.nlm.nih.gov/pubmed/36536756
http://dx.doi.org/10.1016/j.jocs.2022.101926
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author Chatterjee, Sukanya
Roychowdhury, Jishnu
Dey, Anilesh
author_facet Chatterjee, Sukanya
Roychowdhury, Jishnu
Dey, Anilesh
author_sort Chatterjee, Sukanya
collection PubMed
description The limitations of proper detectors for COVID-19 for the proliferating number of patients provoked us to build an auto-diagnosis system to detect COVID-19 infection using only one parameter. Our designed model is based on Deep Convolution Neural Network and considers lung/respiratory sound as the deterministic input for our approach. 'D-Cov19Net' has been trained with 23,592 recordings, begetting an AUC of 0.972 and sensitivity of 0.983 after 100 epochs. The model can be of immense utility in biomedical technology due to its significant accuracy, simplicity, user convenience, feasibility, and faster detection while maintaining social distancing.
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spelling pubmed-97534532022-12-15 D-Cov19Net: A DNN based COVID-19 detection system using lung sound Chatterjee, Sukanya Roychowdhury, Jishnu Dey, Anilesh J Comput Sci Article The limitations of proper detectors for COVID-19 for the proliferating number of patients provoked us to build an auto-diagnosis system to detect COVID-19 infection using only one parameter. Our designed model is based on Deep Convolution Neural Network and considers lung/respiratory sound as the deterministic input for our approach. 'D-Cov19Net' has been trained with 23,592 recordings, begetting an AUC of 0.972 and sensitivity of 0.983 after 100 epochs. The model can be of immense utility in biomedical technology due to its significant accuracy, simplicity, user convenience, feasibility, and faster detection while maintaining social distancing. Elsevier B.V. 2023-01 2022-12-15 /pmc/articles/PMC9753453/ /pubmed/36536756 http://dx.doi.org/10.1016/j.jocs.2022.101926 Text en © 2022 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
Chatterjee, Sukanya
Roychowdhury, Jishnu
Dey, Anilesh
D-Cov19Net: A DNN based COVID-19 detection system using lung sound
title D-Cov19Net: A DNN based COVID-19 detection system using lung sound
title_full D-Cov19Net: A DNN based COVID-19 detection system using lung sound
title_fullStr D-Cov19Net: A DNN based COVID-19 detection system using lung sound
title_full_unstemmed D-Cov19Net: A DNN based COVID-19 detection system using lung sound
title_short D-Cov19Net: A DNN based COVID-19 detection system using lung sound
title_sort d-cov19net: a dnn based covid-19 detection system using lung sound
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753453/
https://www.ncbi.nlm.nih.gov/pubmed/36536756
http://dx.doi.org/10.1016/j.jocs.2022.101926
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