Date of Award
Master of Science (MS)
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.
CNN, Covid-19, Deep learning, DenseNet121, ResNet-50, VGG-16
Number of Pages
University of South Dakota
Mamun, Muntasir, "Analyzing Cough Sounds for the Evidence of Covid-19 using Deep Learning Models" (2022). Dissertations and Theses. 86.