The whole world is facing a health crisis, that is unique in its kind, due to the COVID-19 pandemic. As the coronavirus continues spreading, researchers are concerned by providing or help provide solutions to save lives and to stop the pandemic outbreak. Among others, artificial intelligence (AI) has been adapted to address the challenges caused by pandemic. In this article, we design a deep learning system to extract features and detect COVID-19 from chest X-ray images. Three powerful networks, namely ResNet50, InceptionV3, and VGG16, have been fine-tuned on an enhanced dataset, which was constructed by collecting COVID-19 and normal chest X-ray images from different public databases. We applied data augmentation techniques to artificially generate a large number of chest X-ray images: Random Rotation with an angle between − 10 and 10 degrees, random noise, and horizontal flips. Experimental results are encouraging: the proposed models reached an accuracy of 97.20 % for Resnet50, 98.10 % for InceptionV3, and 98.30 % for VGG16 in classifying chest X-ray images as Normal or COVID-19. The results show that transfer learning is proven to be effective, showing strong performance and easy-to-deploy COVID-19 detection methods. This enables automatizing the process of analyzing X-ray images with high accuracy and it can also be used in cases where the materials and RT-PCR tests are limited.

由于 COVID-19 大流行，整个世界都面临着一场独一无二的健康危机。随着冠状病毒的继续传播，研究人员通过提供或帮助提供解决方案来挽救生命和阻止大流行爆发而感到担忧。其中，人工智能 (AI) 已适应应对大流行带来的挑战。在本文中，我们设计了一个深度学习系统来从胸部 X 射线图像中提取特征并检测 COVID-19。三个强大的网络，即 ResNet50、InceptionV3 和 VGG16，已经在一个增强的数据集上进行了微调，该数据集是通过从不同的公共数据库收集 COVID-19 和正常的胸部 X 光图像构建的。我们应用数据增强技术人工生成大量胸部 X 光图像：角度在 − 10 到 10 度之间的随机旋转，随机噪声和水平翻转。实验结果令人鼓舞：在将胸部 X 射线图像分类为 Normal 或 COVID-19 时，所提出的模型达到了 Resnet50 的 97.20%、InceptionV3 的 98.10% 和 VGG16 的 98.30% 的准确度。结果表明，迁移学习被证明是有效的，表现出强大的性能和易于部署的 COVID-19 检测方法。这可以实现高精度 X 射线图像分析过程的自动化，也可用于材料和 RT-PCR 测试受限的情况。表现出强大的性能和易于部署的 COVID-19 检测方法。这可以实现高精度 X 射线图像分析过程的自动化，也可用于材料和 RT-PCR 测试受限的情况。表现出强大的性能和易于部署的 COVID-19 检测方法。这可以实现高精度 X 射线图像分析过程的自动化，也可用于材料和 RT-PCR 测试受限的情况。