COVID-19 image segmentation based on deep learning and ensemble learning

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.

Metadaten
Author:	Philip Meyer, Dominik Müller ORCiD GND, Iñaki Soto-Rey ORCiD, Frank Kramer ORCiD GND
URN:	urn:nbn:de:bvb:384-opus4-1046251
Frontdoor URL	https://opus.bibliothek.uni-augsburg.de/opus4/104625
ISBN:	978-1-64368-184-9OPAC
ISSN:	0926-9630OPAC
ISSN:	1879-8365OPAC
Parent Title (English):	Public health and informatics
Publisher:	IOS Press
Place of publication:	Amsterdam
Editor:	John Mantas, Lăcrămioara Stoicu-Tivadar, Catherine Chronaki, Arie Hasman, Patrick Weber, Parisis Gallos, Mihaela Crişan-Vida, Emmanouil Zoulias, Oana Sorina Chirila
Type:	Part of a Book
Language:	English
Year of first Publication:	2021
Publishing Institution:	Universität Augsburg
Release Date:	2023/06/12
First Page:	518
Last Page:	519
Series:	Studies in Health Technology and Informatics ; 281
DOI:	https://doi.org/10.3233/shti210223
Institutes:	Fakultät für Angewandte Informatik
	Fakultät für Angewandte Informatik / Institut für Informatik
	Medizinische Fakultät
	Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für IT-Infrastrukturen für die Translationale Medizinische Forschung
	Medizinische Fakultät / Universitätsklinikum
Dewey Decimal Classification:	0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):	CC-BY-NC 4.0: Creative Commons: Namensnennung - Nicht kommerziell (mit Print on Demand)

Open Access