The new coronavirus ( COVID-19 ) , declared by the World Health Organization as a pandemic, has infected more than 1 million people and killed more than 50 thousand. An infection caused by COVID-19 can develop into pneumonia, which can be detected by a chest X-ray exam and should be treated appropriately. In this work, we propose an automatic detection method for COVID-19 infection based on chest X-ray images. The datasets constructed for this study are composed of 194 X-ray images of patients diagnosed with coronavirus and 194 X-ray images of healthy patients. Since few images of patients with COVID-19 are publicly available, we apply the concept of transfer learning for this task. We use different architectures of convolutional neural networks ( CNNs ) trained on ImageNet, and adapt them to behave as feature extractors for the X-ray images. Then, the CNNs are combined with consolidated machine learning methods, such as k-Nearest Neighbor, Bayes, Random Forest, multilayer perceptron ( MLP ) , and support vector machine ( SVM ) . The results show that, for one of the datasets, the extractor-classifier pair with the best performance is the MobileNet architecture with the SVM classifier using a linear kernel, which achieves an accuracy and an F1-score of 98.5 & . For the other dataset, the best pair is DenseNet201 with MLP, achieving an accuracy and an F1-score of 95.6 & . Thus, the proposed approach demonstrates efficiency in detecting COVID-19 in X-ray images.

中文翻译：

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通过转移学习使用胸部X射线图像自动检测COVID-19感染

被世界卫生组织宣布为大流行的新冠状病毒（COVID-19）已感染超过100万人，并杀死了5万人。由COVID-19引起的感染可发展为​​肺炎，可通过胸部X线检查检查发现，应适当治疗。在这项工作中，我们提出了一种基于胸部X射线图像的COVID-19感染自动检测方法。为该研究构建的数据集由被诊断出冠状病毒的患者的194幅X射线图像和健康患者的194幅X射线图像组成。由于很少有COVID-19患者的图像公开可用，因此我们将转移学习的概念用于此任务。我们使用在ImageNet上训练的卷积神经网络（CNN）的不同体系结构，并使它们适应性地充当X射线图像的特征提取器。然后，CNN与合并的机器学习方法相结合，例如k最近邻，贝叶斯，随机森林，多层感知器（MLP）和支持向量机（SVM）。结果表明，对于其中一个数据集，性能最佳的提取器-分类器对是带有线性内核的SVM分类器的MobileNet体系结构，其准确性和F1得分为98.5＆。对于其他数据集，最佳对是带有MLP的DenseNet201，可实现准确度和F1得分95.6＆。因此，提出的方法证明了在X射线图像中检测COVID-19的效率。对于其中一个数据集，性能最佳的提取器-分类器对是带有线性内核的SVM分类器的MobileNet体系结构，该分类器的准确性和F1得分为98.5＆。对于其他数据集，最佳对是带有MLP的DenseNet201，可实现准确度和F1得分95.6＆。因此，提出的方法证明了在X射线图像中检测COVID-19的效率。对于其中一个数据集，性能最佳的提取器-分类器对是带有线性内核的SVM分类器的MobileNet体系结构，该分类器的准确性和F1得分为98.5＆。对于其他数据集，最佳对是带有MLP的DenseNet201，可实现准确度和F1得分95.6＆。因此，提出的方法证明了在X射线图像中检测COVID-19的效率。