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Updated: Sep 22, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
A COVID-19 CXR image recognition method based on MSA-DDCovidNet
Wei Wang1, Wendi Huang1, Xin Wang1
1School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China.
Abstract:
Currently, coronavirus disease 2019 (COVID-19) has not been contained. It is a safe and effective way to detect infected persons in chest X-ray (CXR) images based on deep learning methods. To solve the above problem, the dual-path multi-scale fusion (DMFF) module and dense dilated depth-wise separable (D3S) module are used to extract shallow and deep features, respectively. Based on these two modules and multi-scale spatial attention (MSA) mechanism, a lightweight convolutional neural network model, MSA-DDCovidNet, is designed. Experimental results show that the accuracy of the MSA-DDCovidNet model on COVID-19 CXR images is as high as 97.962%, In addition, the proposed MSA-DDCovidNet has less computation complexity and fewer parameter numbers. Compared with other methods, MSA-DDCovidNet can help diagnose COVID-19 more quickly and accurately.
