2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDCHLG-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
SessionCHLG-1: COVID-19 Diagnosis
LocationZoom
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
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.