论文标题
将深度学习与大型汇总数据集一起进行咳嗽的COVID-19分类
Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough
论文作者
论文摘要
Covid-19的大流行一直是最近历史上最具破坏性的事件之一，夺去了全球超过500万人的生活。即使在全球疫苗的分布中，显然需要负担得起，可靠且可访问的筛查技术，以服务世界各地无法获得西药的部分。人工智能可以利用咳嗽声音作为COVID-19诊断的主要筛查模式提供解决方案。本文介绍了多种模型，这些模型在当前在学术文献中介绍的最大评估数据集上取得了相对可观的性能。通过研究自我监督的学习模型（ROC曲线下的区域，AUC = 0.807）和卷积的新网络（CNN）模型（AUC = 0.802），我们观察到具有有限数据集的模型偏差的可能性。此外，我们观察到绩效随训练数据的规模而增加，表明需要全球数据收集，以帮助以非传统手段对抗Covid-19的大流行。
The Covid-19 pandemic has been one of the most devastating events in recent history, claiming the lives of more than 5 million people worldwide. Even with the worldwide distribution of vaccines, there is an apparent need for affordable, reliable, and accessible screening techniques to serve parts of the World that do not have access to Western medicine. Artificial Intelligence can provide a solution utilizing cough sounds as a primary screening mode for COVID-19 diagnosis. This paper presents multiple models that have achieved relatively respectable performance on the largest evaluation dataset currently presented in academic literature. Through investigation of a self-supervised learning model (Area under the ROC curve, AUC = 0.807) and a convolutional nerual network (CNN) model (AUC = 0.802), we observe the possibility of model bias with limited datasets. Moreover, we observe that performance increases with training data size, showing the need for the worldwide collection of data to help combat the Covid-19 pandemic with non-traditional means.
