一种基于MSA-DDCovidNet的新冠肺炎胸部X光图像识别方法。
A COVID-19 CXR image recognition method based on MSA-DDCovidNet.
作者信息
Wang Wei, Huang Wendi, Wang Xin, Zhang Peng, Zhang Nian
机构信息
School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China.
School of Electronics and Communications Engineering Sun Yat-sen University Shenzhen China.
出版信息
IET Image Process. 2022 Jun 19;16(8):2101-2113. doi: 10.1049/ipr2.12474. Epub 2022 Mar 15.
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.
目前，2019冠状病毒病（COVID-19）尚未得到控制。基于深度学习方法在胸部X光（CXR）图像中检测感染者是一种安全有效的方法。为了解决上述问题，分别使用双路径多尺度融合（DMFF）模块和密集扩张深度可分离（D3S）模块来提取浅层和深层特征。基于这两个模块和多尺度空间注意力（MSA）机制，设计了一种轻量级卷积神经网络模型MSA-DDCovidNet。实验结果表明，MSA-DDCovidNet模型在COVID-19 CXR图像上的准确率高达97.962%，此外，所提出的MSA-DDCovidNet具有较低的计算复杂度和较少的参数数量。与其他方法相比，MSA-DDCovidNet可以帮助更快、更准确地诊断COVID-19。
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