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Exploring Artificial Intelligence in COVID-19 Detection Through Cough Sound Analysis

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posted on 2024-07-12, 20:58 authored by Sarah Jane Kho
Previous studies feature cough sound recordings and AI for COVID-19 pre-screening. These studies often neglect cough segmentation and relies on single-source datasets. This study introduces a deep learning framework with cough segmentation methods using 15 MFCC coefficients and a Mini VGGNet model. Cross-dataset training and testing with Cambridge, Coswara, and NIH Malaysia datasets were also conducted. Findings highlight manual segmentation (0.4-second duration, 0.1-second overlap) as the optimal cough segmentation method. The best performing model was trained on 80% of merged dataset (triple sourced) and tested on 20% of the Cambridge set achieving 0.921 test-accuracy, 0.973 AUC, 0.910 precision, and 0.910 recall.

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  • Thesis (Masters by research)

Thesis note

Thesis submitted for the Degree of Masters by Research, Swinburne University of Technology, Sarawak, 2024.

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Copyright © 2024 Sarah Jane Kho.

Supervisors

Patrick Then

Language

eng

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