  Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest
X-ray (CXR) images is critical. To reduce intra- and inter-observer
variability, during the radiological assessment, computer-aided diagnostic
tools have been utilized to supplement medical decision-making and subsequent
disease management. Computational methods with high accuracy and robustness are
required for rapid triaging of patients and aiding radiologists in the
interpretation of the collected data. In this study, we propose a novel
multi-feature fusion network using parallel attention blocks to fuse the
original CXR images and local-phase feature-enhanced CXR images at
multi-scales. We examine our model on various COVID-19 datasets acquired from
different organizations to assess the generalization ability. Our experiments
demonstrate that our method achieves state-of-art performance and has improved
generalization capability, which is crucial for widespread deployment.
