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

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

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
27 May 2021
Historique:
entrez: 27 5 2021
pubmed: 28 5 2021
medline: 1 6 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 34042629
pii: SHTI210223
doi: 10.3233/SHTI210223
doi:

Types de publication

Journal Article

Langues

eng

Pagination

518-519

Auteurs

Philip Meyer (P)

IT-Infrastructure for Translational Medical Research, University of Augsburg.

Dominik Müller (D)

IT-Infrastructure for Translational Medical Research, University of Augsburg.

Iñaki Soto-Rey (I)

IT-Infrastructure for Translational Medical Research, University of Augsburg.

Frank Kramer (F)

IT-Infrastructure for Translational Medical Research, University of Augsburg.

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