Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3418-3421. doi: 10.1109/EMBC48229.2022.9871615.
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.
我们提出了一个统一的系统，该系统具有数据增强、ImageNet 预训练的 ResNet-50、代价敏感损失、深度集成学习和不确定性估计的核心组件，以使用声学证据快速一致地检测 COVID-19。为了提高模型识别少数类别的能力，我们采用了数据增强和代价敏感损失（感染样本）。在 COVID-19 检测挑战中，发现经过 ImageNet 预训练的 ResNet-50 非常有效。统一框架还集成了深度集成学习和不确定性估计，以整合来自各种基础分类器的预测，从而实现泛化和可靠性。我们使用 DiCOVA2021 挑战数据集进行了一系列测试，以评估我们提出的方法的效果，结果表明我们的方法的 AUC-ROC 为 85.43%，这表明它是一种有前途的 COVID-19 检测方法。统一框架还表明，音频可用于快速诊断不同的呼吸障碍。
