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A critic evaluation of methods for COVID-19 automatic detection from X-ray images
Information Fusion ( IF 18.6 ) Pub Date : 2021-04-30 , DOI: 10.1016/j.inffus.2021.04.008
Gianluca Maguolo 1 , Loris Nanni 1
Affiliation  

In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. 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 the lungs from the images by turning to black the center of the X-Ray scan and training our classifiers only on the outer part of the images. Hence, we deduce that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the dataset that are not correlated to the presence of COVID-19. Finally, we show that creating a fair testing protocol is a challenging task, and we provide a method to measure how fair a specific testing protocol is. In the future research we suggest to check the fairness of a testing protocol using our tools and we encourage researchers to look for better techniques than the ones that we propose.



中文翻译:

从 X 射线图像自动检测 COVID-19 的方法的批判性评估

在本文中,我们比较和评估了最近文献中用于从 X 射线图像自动诊断 COVID-19 的不同测试协议。我们表明,使用不包含大部分肺部的 X 射线图像可以获得类似的结果。我们能够通过将 X 射线扫描的中心变黑并仅在图像的外部训练我们的分类器来从图像中移除肺部。因此,我们推断用于识别的几个测试协议是不公平的,并且神经网络正在学习数据集中与 COVID-19 的存在无关的模式。最后,我们表明创建公平的测试协议是一项具有挑战性的任务,我们提供了一种方法来衡量特定测试协议的公平程度。

更新日期:2021-05-13
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