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Effects of preprocessing in slice-level classification of interstitial lung disease based on deep convolutional networks
KTH, School of Technology and Health (STH).
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0002-7750-1917
2018 (English)In: VipIMAGE 2017: Proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017, Springer Netherlands, 2018, Vol. 27, p. 624-629Conference paper, Published paper (Refereed)
Abstract [en]

Several preprocessing methods are applied to the automatic classification of interstitial lung disease (ILD). The proposed methods are used for the inputs to an established convolutional neural network in order to investigate the effect of those preprocessing techniques to slice-level classification accuracy. Experimental results demonstrate that the proposed preprocessing methods and a deep learning approach outperformed the case of the original images input to deep learning without preprocessing.

Place, publisher, year, edition, pages
Springer Netherlands, 2018. Vol. 27, p. 624-629
Series
Lecture Notes in Computational Vision and Biomechanics, ISSN 2212-9391 ; 27
Keywords [en]
Deep convolutional network, Deep learning, Preprocessing, Transfer learning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-218925DOI: 10.1007/978-3-319-68195-5_67ISI: 000437032100067Scopus ID: 2-s2.0-85032391888ISBN: 978-3-319-68194-8 (print)OAI: oai:DiVA.org:kth-218925DiVA, id: diva2:1161953
Conference
The VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017
Note

QC 20171201

Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2018-07-23Bibliographically approved

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Smedby, Örjan

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf