A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images
This addresses the need for faster and more accurate early screening of COVID-19 patients, though it is incremental as it builds on existing deep learning methods with added explainability.
The paper tackles the problem of diagnosing COVID-19 from chest X-ray images by proposing an explainable two-step approach that first detects lung anomalies and then diagnoses the illness, achieving performance comparable to expert human radiologists.
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.