We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-19 using acoustic evidence. To increase the model's capacity to identify a minority class, data augmentation and cost-sensitive loss are incorporated (infected samples). In the COVID-19 detection challenge, ImageNet-pretrained ResNet-50 has been found to be effective. The unified framework also integrates deep ensemble learning and uncertainty estimation to integrate predictions from various base classifiers for generalisation and reliability. We ran a series of tests using the DiCOVA2021 challenge dataset to assess the efficacy of our proposed method, and the results show that our method has an AUC-ROC of 85.43 percent, making it a promising method for COVID-19 detection. The unified framework also demonstrates that audio may be used to quickly diagnose different respiratory disorders.

翻译：我们建议建立一个包含数据增强、图像网预先培训的ResNet-50、成本敏感损失、深合体学习和不确定性估算等核心组成部分的统一系统,以便利用声学证据快速和一致地探测COVID-19。为了提高模型识别少数类别的能力,数据增强和成本敏感损失(感染样本)被纳入了模型(感染样本)。在COVID-19检测挑战中,图像网预先培训的ResNet-50被发现是有效的。统一框架还整合了深层次的共通性学习和不确定性估算,以整合各种基础分类器的预测,以便普遍化和可靠性。我们用DiCOVA2021挑战数据集进行了一系列测试,以评估我们拟议方法的功效,结果显示我们的方法有85.43%的AUC-ROC,这是探测COVID-19的一个很有希望的方法。统一框架还表明,声音可用于快速诊断不同的呼吸系统紊乱。