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In this paper, we compare and evaluate different testing protocols used forautomatic COVID-19 diagnosis from X-Ray images in the recent literature. Weshow that similar results can be obtained using X-Ray images that do notcontain most of the lungs. We are able to remove the lungs from the images byturning to black the center of the X-Ray scan and training our classifiers onlyon the outer part of the images. Hence, we deduce that several testingprotocols for the recognition are not fair and that the neural networks arelearning patterns in the dataset that are not correlated to the presence ofCOVID-19. Finally, we show that creating a fair testing protocol is achallenging task, and we provide a method to measure how fair a specifictesting protocol is. In the future research we suggest to check the fairness ofa testing protocol using our tools and we encourage researchers to look forbetter techniques than the ones that we propose.

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