Zhang Xin, Lu Siyuan, Wang Shui-Hua, Yu Xiang, Wang Su-Jing, Yao Lun, Pan Yi, Zhang Yu-Dong
Department of Medical Imaging, The Fourth People's Hospital of Huai'an, Huai'an, 223002 China.
School of Informatics, University of Leicester, Leicester, LE1 7RH UK.
J Comput Sci Technol. 2022;37(2):330-343. doi: 10.1007/s11390-020-0679-8. Epub 2022 Mar 31.
COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and enhance it with the following two aspects: 1) adding batch normalization to help accelerate the training, reducing the internal covariance shift; 2) replacing the fully connected layer in AlexNet with three classifiers: SNN, ELM, and RVFL. Therefore, we have three novel models from the deep COVID network (DC-Net) framework, which are named DC-Net-S, DC-Net-E, and DC-Net-R, respectively. After comparison, we find the proposed DC-Net-R achieves an average accuracy of 90.91% on a private dataset (available upon email request) comprising of 296 images while the specificity reaches 96.13%, and has the best performance among all three proposed classifiers. In addition, we show that our DC-Net-R also performs much better than other existing algorithms in the literature.
The online version contains supplementary material available at 10.1007/s11390-020-0679-8.
新型冠状病毒肺炎（COVID-19）是一种具有传染性的感染病，对全球经济和我们的日常生活产生严重影响。准确诊断COVID-19对会诊医生、患者和放射科医生都很重要。在本研究中，我们以深度学习网络AlexNet作为基础，并从以下两个方面对其进行增强：1）添加批归一化以帮助加速训练，减少内部协方差偏移；2）用三个分类器（SNN、ELM和RVFL）替换AlexNet中的全连接层。因此，我们从深度COVID网络（DC-Net）框架中得到了三个新颖的模型，分别命名为DC-Net-S、DC-Net-E和DC-Net-R。经过比较，我们发现所提出的DC-Net-R在一个由296张图像组成的私有数据集（可通过电子邮件请求获取）上实现了90.91%的平均准确率，特异性达到96.13%，并且在所有三个提出的分类器中表现最佳。此外，我们表明我们的DC-Net-R在文献中也比其他现有算法表现得好得多。
在线版本包含可在10.1007/s11390-020-0679-8获取的补充材料。
