Name
Abstract | ||
|---|---|---|
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. (C) 2020 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.asoc.2020.106885 | APPLIED SOFT COMPUTING |
Keywords | DocType | Volume |
COVID-19, Lung CT images, Deep learning, Ensemble learning | Journal | 98 |
ISSN | Citations | PageRank |
1568-4946 | 9 | 0.78 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Zhou | 1 | 9 | 1.45 |
| Huiling Lu | 2 | 26 | 6.09 |
| Zaoli Yang | 3 | 21 | 6.18 |
| Shi Qiu | 4 | 250 | 29.03 |
| Bingqiang Huo | 5 | 9 | 0.78 |
| Yali Dong | 6 | 9 | 0.78 |