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Analyzing Cough Sounds for the Evidence of COVID-19 Using Deep Learning Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analyzing Cough Sounds for the Evidence of COVID-19 Using Deep Learning Models./
Author:	Mamun, Muntasir.
Description:	1 online resource (83 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-06.
Contained By:	Masters Abstracts International84-06.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29995109click for full text (PQDT)
ISBN:	9798357581655

Analyzing Cough Sounds for the Evidence of COVID-19 Using Deep Learning Models.
Mamun, Muntasir.

Analyzing Cough Sounds for the Evidence of COVID-19 Using Deep Learning Models. - 1 online resource (83 pages)

Source: Masters Abstracts International, Volume: 84-06.

Thesis (M.S.)--University of South Dakota, 2022.

Includes bibliographical references

Early detection of infectious disease is the must to prevent/avoid multiple infections, and COVID-19 is an example. When dealing with COVID-19 pandemic, Cough is still ubiquitously presented as one of the key symptoms in both severe and non-severe COVID-19 infections, even though symptoms appear differently in different sociodemographic categories. By realizing the importance of clinical studies, analyzing cough sounds using AI-driven tools could help add more values when it comes to decision-making. Moreover, for mass screening and to serve resource constrained regions, AI-driven tools are the must. In this thesis, Convolutional Neural Network (CNN) tailored deep learning models are studied to analyze cough sounds to detect the possible evidence of COVID-19. In addition to custom CNN, pre-trained deep learning models (e.g., Vgg-16, Resnet-50, MobileNetV1, and DenseNet121) are employed on a publicly available dataset. In our findings, custom CNN performed comparatively better than pre-trained deep learning models.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798357581655Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

CNNIndex Terms--Genre/Form:

542853
Electronic books.

Analyzing Cough Sounds for the Evidence of COVID-19 Using Deep Learning Models.
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520 $a Early detection of infectious disease is the must to prevent/avoid multiple infections, and COVID-19 is an example. When dealing with COVID-19 pandemic, Cough is still ubiquitously presented as one of the key symptoms in both severe and non-severe COVID-19 infections, even though symptoms appear differently in different sociodemographic categories. By realizing the importance of clinical studies, analyzing cough sounds using AI-driven tools could help add more values when it comes to decision-making. Moreover, for mass screening and to serve resource constrained regions, AI-driven tools are the must. In this thesis, Convolutional Neural Network (CNN) tailored deep learning models are studied to analyze cough sounds to detect the possible evidence of COVID-19. In addition to custom CNN, pre-trained deep learning models (e.g., Vgg-16, Resnet-50, MobileNetV1, and DenseNet121) are employed on a publicly available dataset. In our findings, custom CNN performed comparatively better than pre-trained deep learning models.
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