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Early screening of patients is a critical issue in order to assess immediateand fast responses against the spread of COVID-19. The use of nasopharyngealswabs has been considered the most viable approach; however, the result is notimmediate or, in the case of fast exams, sufficiently accurate. Using ChestX-Ray (CXR) imaging for early screening potentially provides faster and moreaccurate response; however, diagnosing COVID from CXRs is hard and we shouldrely on deep learning support, whose decision process is, on the other hand,"black-boxed" and, for such reason, untrustworthy. We propose an explainabletwo-step diagnostic approach, where we first detect known pathologies(anomalies) in the lungs, on top of which we diagnose the illness. Our approachachieves promising performance in COVID detection, compatible with expert humanradiologists. All of our experiments have been carried out bearing in mindthat, especially for clinical applications, explainability plays a major rolefor building trust in machine learning algorithms.

A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images