Document Type
Thesis
Date of Award
2022
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
KC Santosh
Abstract
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.
Subject Categories
Computer Sciences
Keywords
CNN, Covid-19, Deep learning, DenseNet121, ResNet-50, VGG-16
Number of Pages
82
Publisher
University of South Dakota
Recommended Citation
Mamun, Muntasir, "Analyzing Cough Sounds for the Evidence of Covid-19 using Deep Learning Models" (2022). Dissertations and Theses. 86.
https://red.library.usd.edu/diss-thesis/86