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