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Effect of Various Image Information in Polyp Classification by Deeping Learning with Small Dataset

  • Tianjin University

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

4 Scopus citations

Abstract

Clinical colonoscopy is the gold standard for polyp detection and resection in clinic. A main challenge in the polyp identification by deep learning is the limited patients number. In this paper, we discussed the usage of various image layers which provide and emphasize different image information in the polyp recognition and polyp classification. The results showed that comparing with the intensity image in RGB color space, the gradient images and images in chromaticity color space may provide more critical character to the deep learning model, and achieve a better performance with small training dataset.

Original languageEnglish
Title of host publicationICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728123455
DOIs
StatePublished - Dec 2019
Event2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 - Chongqing, China
Duration: Dec 11 2019Dec 13 2019

Publication series

NameICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019

Conference

Conference2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Country/TerritoryChina
CityChongqing
Period12/11/1912/13/19

Keywords

  • colonoscopy
  • color space
  • deep learning
  • image layer
  • polyp

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