| Paper ID | CHLG-1.2 |
| Paper Title |
Diagnosing COVID‐19 from CT Images based on an Ensemble Learning Framework |
| Authors |
Bingyang Li, Qi Zhang, Yinan Song, Zhicheng Zhao, Zhu Meng, Fei Su, Beijing University of Posts and Telecommunications, China |
| Session | CHLG-1: COVID-19 Diagnosis |
| Location | Zoom |
| Session Time: | Monday, 07 June, 09:30 - 12:00 |
| Presentation Time: | Monday, 07 June, 09:30 - 12:00 |
| Presentation |
Poster
|
| Topic |
Grand Challenge: COVID-19 Diagnosis |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Research on automated diagnosis of Coronavirus Disease 2019 (COVID-19) has increased in recent months. SPGC COVID19 aims at classifying the grouped images of the same patient into COVID, Community Acquired Pneumonia(CAP) or normal. In this paper, we propose a novel ensemble learning framework to solve this problem. Moreover, adaptive boosting and dataset clustering algorithms are introduced to improve the classification performance. In our experiments, we demonstrate that our framework is superior to existing networks in terms of both accuracy and sensitivity. |
