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