PDF(Pubmed)
Abstract：
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.
摘要：
在威胁所有人生命的2019年全球大流行冠状病毒病（COVID-19）的背景下，在有症状的患者中实现COVID-19的早期检测至关重要.在本文中,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射线图像中准确识别COVID-19，主要集中于强大和健壮的特征学习能力。特别是,选择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骨干，之后，应用具有不同扩张率的跳跃连接扩张卷积，以实现高级语义和低级详细信息之间的充分特征融合。在两个公共COVID-19射线照相数据库上的实验结果表明，拟议的Cov-Net在准确的COVID-19识别中的实用性，准确率分别为0.9966和0.9901。此外，在相同的实验条件下，提出的Cov-Net优于其他六种最先进的计算机视觉算法，从方法论的角度验证了Cov-Net在构建高区别性特征方面的优越性和竞争力。因此，认为提出的Cov-Net具有良好的泛化能力，可以应用于其他CAD场景。因此,可以得出结论，这项工作既具有为放射科医生提供可靠参考的实用价值，也具有开发方法以构建具有强表示能力的鲁棒特征的理论意义。
