In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images. We show that similar results can be obtained using X-Ray images that do not contain most of the lungs. We are able to remove them from the images by turning to black the center of the scan. Hence, we deduce that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the images that are not correlated to the presence of COVID-19. We propose a new testing protocol that consists in using different datasets for training and testing, and we provide a method to measure how fair is a specific testing protocol. We suggest to follow the proposed protocol in the future research, and provide tools to better interpret the results of a classifier.

本研究比较和评估了近期文献中用于自动 COVID-19 诊断的不同测试协议。我们通过黑化 X 射线扫描的中心并只在图像的外部部分训练分类器的方式移除图像中的肺部，得出了使用不包含大部分肺部的 X 射线图像可以获得类似结果的结论。因此，我们推断出某些识别测试协议不公平，并且神经网络正在学习与 COVID-19 存在无关的数据集模式。最后，我们提供了一种衡量测试协议公平性的方法，并鼓励研究人员寻找比我们所提出的更好的技术。

一种 COVID-19 自动检测 X 光图像的方法的评论评估