Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks.
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks.
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DOI：
10.1007/s10044-021-00984-y
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Pamuk Z
中科院分区：
文献类型：
--
作者：
Narin A;Kaya C;Pamuk Z
根据世界卫生组织的统计，2019年新型冠状病毒病（COVID-19）以中国为起点，在其他国家的人群中迅速传播，全球病例接近约101，917，147例。由于每天的病例不断增加，医院提供的COVID-19检测试剂盒数量有限。因此，有必要实施自动检测系统作为快速替代诊断选项，以防止COVID-19在人群中传播。在这项研究中，提出了五种基于预训练卷积神经网络的模型（ResNet 50，ResNet 101，ResNet 152，InceptionV 3和Inception-ResNetV 2），用于使用胸部X射线照片检测冠状病毒肺炎感染的患者。我们通过五重交叉验证实现了三种不同的二元分类，分为四类（COVID-19、正常（健康）、病毒性肺炎和细菌性肺炎）。考虑到所获得的性能结果，可以看出，在其他四个使用的模型中，预训练的ResNet 50模型提供了最高的分类性能（Dataset-1的96.1%准确率，Dataset-2的99.5%准确率和Dataset-3的99.7%准确率）。
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 101,917,147 cases worldwide according to the statistics of World Health Organization. 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 five-fold cross-validation. Considering the performance results obtained, it has been 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.
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