Image features for automated colorectal polyp classification based on clinical prediction models

M.C.A. van Grinsven, Thom Scheeve, Ramon-Michel Schreuder, Fons van der Sommen, Erik J. Schoon, Peter de With

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

1 Citation (Scopus)

Abstract

Accurate endoscopic differentiation on resection of colorectal polyps (CRPs) (resect-discard or diagnose-leave strategies) increases cost-efficiency and reduces patient risk. We aim to develop a classification algorithm for automated differentiation of CRPs, by following the validated clinical Work-group serrAted polypS and Polyposis (WASP) classification scheme. Quantitative image features are investigated for each individual WASP criterion and classification is performed by conventional SVM. The technical WASP model results in areas under the curve of 0.87-0.95 and accuracies of 78-89%. Predicting polyp histology using model-based learning out-performs medical experts (accuracy, 87-93% vs 86-87%). Direct classification predicts more premalignant polyps-as being benign, compared to the automated WASP scheme. These errors do not occur when including ROC characteristics to the WASP model. The proposed WASP model is the first automated system, competing with medical expert classification.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing (ICIP)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages210-214
Number of pages5
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
DOIs
Publication statusPublished - 26 Aug 2019
Event26th IEEE International Conference on Image Processing (ICIP 2019) - Taipei, Taiwan
Duration: 22 Sep 201925 Sep 2019

Conference

Conference26th IEEE International Conference on Image Processing (ICIP 2019)
Abbreviated titleICIP 2019
Country/TerritoryTaiwan
CityTaipei
Period22/09/1925/09/19

Keywords

  • Colorectal polyps
  • Quantitative features
  • Coputer-aided diagnosis
  • SVM
  • Gastroenterology
  • quantitative features
  • gastroenterology
  • computer-aided diagnosis

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