文献类型：期刊文献

英文题名：Images denoising for COVID-19 chest X-ray based on multi-resolution parallel residual CNN

作者：Jiang, Xiaoben[1];Zhu, Yu[1];Zheng, Bingbing[1];Yang, Dawei[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Technol, Shanghai 200237, Peoples R China;[2]Fudan Univ, Zhongshan Hosp, Dept Pulm & Crit Care Med, Shanghai 200032, Peoples R China

年份：2021

卷号：32

期号：4

外文期刊名：MACHINE VISION AND APPLICATIONS

收录：;EI(收录号：20212710588935);WOS:【SCI-EXPANDED(收录号:WOS:000667682900001)】；

基金：The authors greatly appreciate the inancial supports of Shanghai Pujiang Program (20PJ1402400), Zhongshan Hospital Clinical Research Foundation (2016ZSLC05), Science and Technology Commission of Shanghai Municipality (20DZ2261200) and Shanghai Engineer & Technology Research Center of Internet of Things for Respiratory Medicine (20DZ2254400).

语种：英文

外文关键词：COVID-19; CXR images denoising; Multi-resolution; Residual CNN

摘要：Chest X-ray (CXR) is a medical imaging technology that is common and economical to use in clinical. Recently, coronavirus (COVID-19) has spread worldwide, and the second wave is rebounding strongly now with the coming winter that has a detrimental effect on the global economy and health. To make pre-diagnosis of COVID-19 as soon as possible, and reduce the work pressure of medical staff, making use of deep learning networks to detect positive CXR images of infected patients is a critical step. However, there are complex edge structures and rich texture details in the CXR images susceptible to noise that can interfere with the diagnosis of the machines and the doctors. Therefore, in this paper, we proposed a novel multi-resolution parallel residual CNN (named MPR-CNN) for CXR images denoising and special application for COVID-19 which can improve the image quality. The core of MPR-CNN consists of several essential modules. (a) Multi-resolution parallel convolution streams are utilized for extracting more reliable spatial and semantic information in multi-scale features. (b) Efficient channel and spatial attention can let the network focus more on texture details in CXR images with fewer parameters. (c) The adaptive multi-resolution feature fusion method based on attention is utilized to improve the expression of the network. On the whole, MPR-CNN can simultaneously retain spatial information in the shallow layers with high resolution and semantic information in the deep layers with low resolution. Comprehensive experiments demonstrate that our MPR-CNN can better retain the texture structure details in CXR images. Additionally, extensive experiments show that our MPR-CNN has a positive impact on CXR images classification and detection of COVID-19 cases from denoised CXR images.