Machine Learning-Based Colorectal Cancer Detection

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populations. However, before it can be widely applied, significant research priorities need to be addressed. In this study, we present an innovative machine learning-based algorithm which can considerably improve acquisition and analysis of relevant data on colorectal polyps obtained from capsule endoscopy. The algorithm is to match CCE and colonoscopy polyps, based on objective measures of similarity between polyps. our matching algorithm is able to objectively quantify the similarity between CCE and colonoscopy polyps based on their size, morphology and location, and provides a one-to-one unequivocal match between CCE and colonoscopy polyps.
Original languageEnglish
Title of host publicationProceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018 : RACS 2018
PublisherAssociation for Computing Machinery
Publication date9. Oct 2018
ISBN (Electronic)978-1-4503-5885-9
Publication statusPublished - 9. Oct 2018
EventResearch in Adaptive and Convergent Systems - Honolulu, Hawaii, United States
Duration: 9. Oct 201812. Oct 2018


ConferenceResearch in Adaptive and Convergent Systems
Country/TerritoryUnited States
CityHonolulu, Hawaii


  • Capsule endoscopy
  • Colorectal cancer
  • Matching
  • Polyp


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