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

TL;DR

This paper presents an explainable two-step deep learning approach for COVID-19 diagnosis from chest X-ray images, focusing on early screening with high accuracy and interpretability to foster clinical trust.

Contribution

The paper introduces a novel two-step method combining pathology detection and disease diagnosis, enhancing explainability in COVID-19 CXR analysis.

Findings

Achieves performance comparable to expert radiologists

Provides interpretable results through pathology localization

Supports faster COVID-19 screening in clinical settings

Abstract

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.