  Early screening of patients is a critical issue in order to assess immediate
and fast responses against the spread of COVID-19. The use of nasopharyngeal
swabs has been considered the most viable approach; however, the result is not
immediate or, in the case of fast exams, sufficiently accurate. Using Chest
X-Ray (CXR) imaging for early screening potentially provides faster and more
accurate response; however, diagnosing COVID from CXRs is hard and we should
rely on deep learning support, whose decision process is, on the other hand,
"black-boxed" and, for such reason, untrustworthy. We propose an explainable
two-step diagnostic approach, where we first detect known pathologies
(anomalies) in the lungs, on top of which we diagnose the illness. Our approach
achieves promising performance in COVID detection, compatible with expert human
radiologists. All of our experiments have been carried out bearing in mind
that, especially for clinical applications, explainability plays a major role
for building trust in machine learning algorithms.
