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In this paper, we propose a deep residual network-based method, namely theDiCOVA-Net, to identify COVID-19 infected patients based on the acousticrecording of their coughs. Since there are far more healthy people thaninfected patients, this classification problem faces the challenge ofimbalanced data. To improve the model's ability to recognize minority class(the infected patients), we introduce data augmentation and cost-sensitivemethods into our model. Besides, considering the particularity of this task, wedeploy some fine-tuning techniques to adjust the pre-training ResNet50.Furthermore, to improve the model's generalizability, we use ensemble learningto integrate prediction results from multiple base classifiers generated usingdifferent random seeds. To evaluate the proposed DiCOVA-Net's performance, weconducted experiments with the DiCOVA challenge dataset. The results show thatour method has achieved 85.43\% in AUC, among the top of all competing teams.

DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021