Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis
This work addresses the need for rapid and reliable COVID-19 diagnosis to aid radiologists and reduce variability, though it appears incremental as it builds on existing feature fusion and attention methods.
The study tackled the problem of accurately diagnosing COVID-19 from Chest X-ray images by proposing a multi-feature fusion network with parallel attention blocks, achieving state-of-the-art performance and improved generalization across multiple datasets.
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.