Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis

Autor:	Qi, Xiao, Foran, David J., Nosher, John L., Hacihaliloglu, Ilker
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Zdroj:	Machine.Learning.for.Biomedical.Imaging. 2 (2023)
Druh dokumentu:	Working Paper
DOI:	10.59275/j.melba.2023-7e96
Popis:	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. Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:008
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.12988 Zobrazit plný text záznamu View this record from Arxiv