Deep Learning Method for Estimation of Morphological Parameters Based on CT Scans

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In this study, we propose a Convolutional Neural Network (CNN) with an assembly of non-linear fully connected layers for estimating body height and weight using a limited amount of data. This method can predict the parameters within acceptable clinical limits for most of the cases even when trained with limited data.

Original languageEnglish
Title of host publicationCaring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023
EditorsMaria Hagglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindskold, Parisis Gallos
PublisherIOS Press
Pages364-365
Number of pages2
ISBN (Electronic)9781643683881
DOIs
Publication statusPublished - 18 May 2023
Event33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023 - Gothenburg, Sweden
Duration: 22 May 202325 May 2023

Publication series

NameStudies in Health Technology and Informatics
Volume302
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023
Country/TerritorySweden
CityGothenburg
Period22/05/2325/05/23

Bibliographical note

Publisher Copyright:
© 2023 European Federation for Medical Informatics (EFMI) and IOS Press.

Keywords

  • CNN
  • Deep Learning
  • Electronic Health Records (EHRs)
  • Height
  • Weight
  • Neural Networks, Computer
  • Tomography, X-Ray Computed

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