COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning

被引：0

作者：

Meyer, Philip ^{[1
]}

Mueller, Dominik ^{[1
]}

Soto-Rey, Inaki ^{[1
]}

Kramer, Frank ^{[1
]}

机构：

[1] Univ Augsburg, IT Infrastructure Translat Med Res, Augsburg, Germany

来源：

PUBLIC HEALTH AND INFORMATICS, PROCEEDINGS OF MIE 2021 | 2021年 / 281卷

关键词：

COVID-19; segmentation; ensemble learning; computed tomography; deep learning; artificial intelligence;

D O I：

10.3233/SHTI210223

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Medical imaging offers great potential for COVID-19 diagnosis and monitoring. Our work introduces an automated pipeline to segment areas of COVID-19 infection in CT scans using deep convolutional neural networks. Furthermore, we evaluate the performance impact of ensemble learning techniques (Bagging and Augmenting). Our models showed highly accurate segmentation results, in which Bagging achieved the highest dice similarity coefficient.

引用

页码：518 / 519

页数：2

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