A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images

Understand

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.