Diagnosing Covid-19 from CT Images Based on an Ensemble Learning Framework

Bingyang Li; Qi Zhang; Yinan Song; Zhicheng Zhao; Zhu Meng; Fei Su

ICASSP 2021accepted0 citations

Diagnosing Covid-19 from CT Images Based on an Ensemble Learning Framework

Bingyang Li, Qi Zhang, Yinan Song, Zhicheng Zhao, Zhu Meng, Fei Su

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.

BibTeX

@inproceedings{icassp2021_diagnosingcovid1,
  title = {Diagnosing Covid-19 from CT Images Based on an Ensemble Learning Framework},
  author = {Bingyang Li and Qi Zhang and Yinan Song and Zhicheng Zhao and Zhu Meng and Fei Su},
  booktitle = {ICASSP 2021},
  year = {2021}
}