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