The ensemble deep learning model for novel COVID-19 on CT images | |
Zhou, Tao1,3; Lu, Huiling2; Yang, Zaoli4; Qiu, Shi5; Huo, Bingqiang1; Dong, Yali1 | |
作者部门 | 光谱成像技术研究室 |
2021-01 | |
发表期刊 | Applied Soft Computing |
ISSN | 15684946 |
卷号 | 98 |
产权排序 | 5 |
摘要 | The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19. © 2020 |
关键词 | COVID-19 Lung CT images Deep learning Ensemble learning |
DOI | 10.1016/j.asoc.2020.106885 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000603366000004 |
出版者 | Elsevier Ltd |
EI入藏号 | 20204709509999 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/93820 |
专题 | 光谱成像技术研究室 |
通讯作者 | Lu, Huiling |
作者单位 | 1.School of Computer Science and Engineering, North minzu University, Yinchuan; 750021, China; 2.School of Science, Ningxia Medical University, Yinchuan; 750004, China; 3.Ningxia Key Laboratory of Intelligent Information and Big Data Processing, Yinchuan; 750021, China; 4.College of Economics and Management, Beijing University of Technology, Beijing; 100124, China; 5.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China |
推荐引用方式 GB/T 7714 | Zhou, Tao,Lu, Huiling,Yang, Zaoli,et al. The ensemble deep learning model for novel COVID-19 on CT images[J]. Applied Soft Computing,2021,98. |
APA | Zhou, Tao,Lu, Huiling,Yang, Zaoli,Qiu, Shi,Huo, Bingqiang,&Dong, Yali.(2021).The ensemble deep learning model for novel COVID-19 on CT images.Applied Soft Computing,98. |
MLA | Zhou, Tao,et al."The ensemble deep learning model for novel COVID-19 on CT images".Applied Soft Computing 98(2021). |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
The ensemble deep le(1782KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论