Yijia Guan
ABSTRACT
The global outbreak of the COVID-19 pandemic has driven the development of effective and low-cost detection technologies. With an emphasis on methods' economic viability and detection efficacy, researchers have been actively exploring novel technologies in response to it. To address the issue, we have revisited a deep learning-based framework to facilitate the diagnosis of COVID-19 solely through the analysis of cough sounds. We utilized the label of expert diagnoses and employed time stretching as an augmentation method on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM). Trained and Tested on the largest publicly-available COUGHVID dataset, our proposed hybrid CNN-LSTM model showed its performance with a short training period, demonstrating proficiency in discerning between COVID-19-related cough sounds and those of a healthy nature. Our classification model achieved an accuracy of 99.19%, a precision of 94.92%, a recall of 88.61%, a F1 Score of 91.66%, and an AUC score of 96%.
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Index Terms
- Revisiting COVID-19 Diagnosis From Cough Sound: A Hybrid CNN-LSTM Model Utilizing Offline Time Stretching Augmentation
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