论文标题
一项关于使用基于深度学习的方法检测胸部X射线图像中COVID-19的实证研究
An Empirical Study on Detecting COVID-19 in Chest X-ray Images Using Deep Learning Based Methods
论文作者
论文摘要
Covid-19病毒的传播增加了提供测试套件的努力。这些套件的准备不仅是艰难，稀有和昂贵的，而且使用它们是另一个问题。结果表明，除了遇到30％损失的事实外，这些试剂盒还需要一些关键时间才能识别该病毒。在本文中，我们研究了无处不在的X射线图片的用法，用于通过现有的卷积神经网络（CNN）对Covid-19胸部X射线图像进行分类。我们打算训练具有不同CNN体系结构在内的受感染且不感染的胸部X射线，包括VGG19，Densnet-121和Xception。训练这些体系结构会导致不同的精确度，这些精度比测试的方法更快，更精确。
Spreading of COVID-19 virus has increased the efforts to provide testing kits. Not only the preparation of these kits had been hard, rare, and expensive but also using them is another issue. Results have shown that these kits take some crucial time to recognize the virus, in addition to the fact that they encounter with 30% loss. In this paper, we have studied the usage of x-ray pictures which are ubiquitous, for the classification of COVID-19 chest Xray images, by the existing convolutional neural networks (CNNs). We intend to train chest x-rays of infected and not infected ones with different CNNs architectures including VGG19, Densnet-121, and Xception. Training these architectures resulted in different accuracies which were much faster and more precise than usual ways of testing.
