| Literature DB >> 34042629 |
Philip Meyer1, Dominik Müller1, Iñaki Soto-Rey1, Frank Kramer1.
Abstract
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.Entities:
Keywords: COVID-19; artificial intelligence; computed tomography; deep learning; ensemble learning; segmentation
Mesh：
Year: 2021 PMID： 34042629 DOI： 10.3233/SHTI210223
Source DB: PubMed Journal: Stud Health Technol Inform ISSN： 0926-9630