DIAGNOSING COVID-19 FROM CT IMAGES BASED ON AN ENSEMBLE LEARNING FRAMEWORK

被引：5

作者：

Li, Bingyang ^{[1
]}

Zhang, Qi ^{[1
]}

Song, Yinan ^{[1
]}

Zhao, Zhicheng ^{[1
]}

Meng, Zhu ^{[1
]}

Su, Fei ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Beijing, Peoples R China

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) | 2021年

关键词：

COVID-19; Deep Learning; Computed Tomography(CT) Scan Image; CAP; Ensemble Learning;

D O I：

10.1109/ICASSP39728.2021.9413707

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：8563 / 8567

页数：5

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