Abstract
According to the World Health Organization (WHO), since the discovery of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there have been a total of 530,896,347 confirmed cases of COVID-19 of which 6,301,020 deaths have been reported as of June 2022. The virus (SARS-CoV-2) primarily affects the respiratory system causing dry cough to be prominent among other symptoms. Sound and the type of cough are said to contain useful features that can contribute to the diagnosis of a disease. Artificial Intelligence (AI) and signal processing show promising potential in the prediction of pulmonary diseases. This provides a platform for a non-invasive method of screening for COVID-19. In turn, this allows for a faster way to screen for the presence of the virus promoting early detection. This paper studies the usage of AI and signal processing methods for the detection of COVID-19 using cough sounds collected from crowdsourced applications. This paper focuses on three different models trained using a Support Vector Machine (SVM), ResNet, and a traditional Convolutional Neural Network (CNN). The datasets used in this study were obtained from the University of Cambridge and the Coswara project. Preliminary investigations show that for models trained and tested using the Cambridge Dataset, CNN performed the best with an AUC of 0.816 followed by the ResNet model and the SVM model with an AUC of 0.738 and 0.671, respectively. Further investigation shows that the models perform poorly in classifying COVID-19-positive and -negative classes when tested on the Coswara dataset. Testing results show that the SVM model performed the best, with an AUC of 0.564, followed by the ResNet and the CNN models (AUCs = 0.522 and 0.443, respectively). Our future work will focus on improving the models’ performance by applying various data pre-processing and feature extraction methods on the datasets.
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Acknowledgements
The authors would like to thank Swinburne University of Technology, Sarawak Campus, for providing the necessary resources to carry out this study.
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Kho, S.J., Shiong, B.L.C., Tze, V.W., Hui, P.T.H. (2023). Cough Sound Disease Detection with Artificial Intelligence. In: Choo, C.S., Wong, B.T., Sharkawi, K.H.B., Kong, D. (eds) Proceedings of ASEAN-Australian Engineering Congress (AAEC2022). AAEC 2022. Lecture Notes in Electrical Engineering, vol 1072. Springer, Singapore. https://doi.org/10.1007/978-981-99-5547-3_2
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