Li Han, Zeng Nianyin, Wu Peishu, Clawson Kathy
Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361102, China.
School of Computer Science, University of Sunderland, Saint Peter Campus, United Kingdom.
Expert Syst Appl. 2022 Nov 30;207:118029. doi: 10.1016/j.eswa.2022.118029. Epub 2022 Jul 5.
In the context of global pandemic Coronavirus disease 2019 (COVID-19) that threatens life of all human beings, it is of vital importance to achieve early detection of COVID-19 among symptomatic patients. In this paper, a computer aided diagnosis (CAD) model Cov-Net is proposed for accurate recognition of COVID-19 from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust feature learning ability. In particular, a modified residual network with asymmetric convolution and attention mechanism embedded is selected as the backbone of feature extractor, after which skip-connected dilated convolution with varying dilation rates is applied to achieve sufficient feature fusion among high-level semantic and low-level detailed information. Experimental results on two public COVID-19 radiography databases have demonstrated the practicality of proposed Cov-Net in accurate COVID-19 recognition with accuracy of 0.9966 and 0.9901, respectively. Furthermore, within same experimental conditions, proposed Cov-Net outperforms other six state-of-the-art computer vision algorithms, which validates the superiority and competitiveness of Cov-Net in building highly discriminative features from the perspective of methodology. Hence, it is deemed that proposed Cov-Net has a good generalization ability so that it can be applied to other CAD scenarios. Consequently, one can conclude that this work has both practical value in providing reliable reference to the radiologist and theoretical significance in developing methods to build robust features with strong presentation ability.
在威胁全人类生命的全球大流行新型冠状病毒肺炎（COVID-19）背景下，对有症状患者实现COVID-19的早期检测至关重要。本文提出了一种计算机辅助诊断（CAD）模型Cov-Net，用于通过机器视觉技术从胸部X光图像中准确识别COVID-19，该模型主要专注于强大且稳健的特征学习能力。具体而言，选择了一种嵌入非对称卷积和注意力机制的改进残差网络作为特征提取器的主干，之后应用具有不同扩张率的跳跃连接扩张卷积，以在高级语义和低级详细信息之间实现充分的特征融合。在两个公开的COVID-19射线照相数据库上的实验结果证明了所提出的Cov-Net在准确识别COVID-19方面的实用性，准确率分别为0.9966和0.9901。此外，在相同实验条件下，所提出的Cov-Net优于其他六种先进的计算机视觉算法，这从方法论角度验证了Cov-Net在构建高辨别力特征方面的优越性和竞争力。因此，认为所提出的Cov-Net具有良好的泛化能力，可应用于其他CAD场景。因此，可以得出结论，这项工作在为放射科医生提供可靠参考方面具有实际价值，在开发具有强大表示能力的稳健特征构建方法方面具有理论意义。
