论文标题
COVID-CXNET：使用深度学习检测额叶X射线图像中的Covid-19
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning
论文作者
论文摘要
新型冠状病毒筛查感染性的主要临床观察之一是捕获胸部X射线图像。在大多数患者中，胸部X射线均包含异常，例如巩固，这是Covid-19病毒性肺炎的结果。在这项研究中，对使用大型数据集中深卷积神经网络有效检测这种类型的肺炎的成像特征进行了研究。可以证明，简单的模型以及文献中大多数经过预告片的网络，重点介绍了与决策无关的特征。在本文中，收集了来自各种来源的许多胸部X射线图像，并准备了最大的公共访问数据集。最后，使用转移学习范式，众所周知的Chexnet模型用于开发Covid-Cxnet。这个强大的模型能够根据具有精确定位的相关和有意义的特征来检测新型冠状病毒肺炎。 COVID-CXNET是迈向完全自动化且可靠的Covid-19检测系统的一步。
One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image. In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from various sources are collected, and the largest publicly accessible dataset is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized for developing COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.
