The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce pulmonary failures, and chest X-ray imaging becomes one of the major confirmed diagnostic technologies. The very limited number of publicly available samples has rendered the training of the deep neural networks unstable and inaccurate. This study proposed a two-step transfer learning pipeline and a deep residual network framework COVID19XrayNet for the COVID-19 detection problem based on chest X-ray images. COVID19XrayNet firstly tunes the transferred model on a large dataset of chest X-ray images, which is further tuned using a small dataset of annotated chest X-ray images. The final model achieved 0.9108 accuracy. The experimental data also suggested that the model may be improved with more training samples being released.

Graphic abstract

COVID19XrayNet, a two-step transfer learning framework designed for biomedical images.

中文翻译：

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COVID19XrayNet：基于有限数量的胸部X射线图像的COVID-19检测问题的两步转移学习模型。

新型冠状病毒严重急性呼吸系统综合症冠状病毒2（SARS-CoV-2）最近已引起大流行大流行。各种诊断技术已经在积极开发中。新型冠状病毒疾病（COVID-19）可能导致肺功能衰竭，胸部X射线成像已成为主要的已证实诊断技术之一。可公开获取的样本数量非常有限，导致深度神经网络的训练不稳定且不准确。这项研究针对基于胸部X射线图像的COVID-19检测问题，提出了两步转移学习管道和深层残差网络框架COVID19XrayNet。首先，COVID19XrayNet在大型的胸部X射线图像数据集上调整传递的模型，然后使用小型的带注释的胸部X射线图像数据集对模型进行调整。最终模型达到0。9108精度。实验数据还表明，可以通过发布更多训练样本来改进模型。

图形摘要

COVID19XrayNet，专为生物医学图像设计的两步转移学习框架。