论文标题
使用X射线图像和深卷积神经网络自动检测冠状病毒病（COVID-19）
Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks
论文作者
论文摘要
根据欧洲疾病预防与控制中心的统计，在全球范围内，2019年的新型冠状病毒病（Covid-19）在中国起点迅速蔓延，在全球范围内约有34,986,502例。由于每天增加病例，医院可用的COVID-19测试套件数量有限。因此，有必要实施自动检测系统，作为一种快速的替代诊断选项，以防止人们在人们之间蔓延。在这项研究中，已经提出了五个预训练的基于卷积神经网络的模型（RESNET50，RESNET101，RESNET152，INCEPTIONV3和INPECTION-RESNETV2），用于检测使用胸部X射线X光射线照片检测肺炎病毒肺炎病毒感染患者。我们通过使用5倍的交叉验证来实施三种不同的二元分类（Covid-19，正常（健康），病毒性肺炎和细菌性肺炎）。考虑到获得的性能结果，已经看到，预先训练的RESNET50模型提供了最高的分类性能（数据集-1的精度为96.1％，数据集-2的精度为99.5％，而数据集的精度为99.7％，对于其他四个使用的模型中的精度为99.7％）。
The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries, and is approaching approximately 34,986,502 cases worldwide according to the statistics of European Centre for Disease Prevention and Control. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using 5-fold cross validation. Considering the performance results obtained, it has seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.
