Early esophageal cancer detection using RF classifiers

Mark Janse, Fons Van Der Sommen*, Svitlana Zinger, Erik J. Schoon, Peter H.N. De With

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Esophageal cancer is one of the fastest rising forms of cancer in the Western world. Using High-Definition (HD) endoscopy, gastroenterology experts can identify esophageal cancer at an early stage. Recent research shows that early cancer can be found using a state-of-the-art computer-aided detection (CADe) system based on analyzing static HD endoscopic images. Our research aims at extending this system by applying Random Forest (RF) classification, which introduces a confidence measure for detected cancer regions. To visualize this data, we propose a novel automated annotation system, employing the unique characteristics of the previous confidence measure. This approach allows reliable modeling of multi-expert knowledge and provides essential data for real-time video processing, to enable future use of the system in a clinical setting. The performance of the CADe system is evaluated on a 39-patient dataset, containing 100 images annotated by 5 expert gastroenterologists. The proposed system reaches a precision of 75% and recall of 90%, thereby improving the state-of-the-art results by 11 and 6 percentage points, respectively.

Original languageEnglish
Title of host publicationMedical Imaging 2016
Subtitle of host publicationComputer-Aided Diagnosis
EditorsGeorgia D. Tourassi, Samuel G. Armato
PublisherSPIE
ISBN (Electronic)9781510600201
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes
EventMedical Imaging 2016: Computer-Aided Diagnosis - San Diego, United States
Duration: 28 Feb 20162 Mar 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9785
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2016: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego
Period28/02/162/03/16

Keywords

  • Computer-aided detection
  • Esophageal cancer
  • HD endoscopy
  • Random forest

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