@article{Zhang2022, 
author = {Xin Zhang and Siyuan Lu and Shui-Hua Wang and Xiang Yu and Su-Jing Wang and Lun Yao and Yi Pan and Yu-Dong Zhang},
title = {Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture},
year = {2022},
journal = {Journal of Computer Science and Technology},
volume = {37},
number = {2},
pages = {330-343},
keywords = {pneumonia, COVID-19, convolutional neural network, AlexNet, deep learning},
url = {https://www.sciopen.com/article/10.1007/s11390-020-0679-8},
doi = {10.1007/s11390-020-0679-8},
abstract = {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.}
}