Comparative performance of random forest and support vector machine classifiers for detection of colorectal lesions in CT colonography

Janne J. Näppi, Daniele Regge, Hiroyuki Yoshida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A major problem of computer-aided detection (CAD) for computed tomographic colonography (CTC) is that CAD systems display large numbers of false-positive detections, thereby distracting users. Support vector machine (SVM) classifiers have been a popular choice for reducing false-positive detections in CAD systems. Recently, random forests (RF) have emerged as a novel type of highly accurate classifier. We compared the relative performance of RF and SVM classifiers in automated detection of colorectal lesions in CTC. The CAD system was trained with the CTC data of 123 patients and tested with an independent set of 737 patients. The results indicate that the performance of an RF classifier compares favorably with that of an SVM classifier in CTC.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages27-34
Number of pages8
Volume7029 LNCS
DOIs
Publication statusPublished - 2012
Event3rd International Workshop on Computational and Clinical Applications in Abdominal Imaging, Held in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 18 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7029 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Workshop on Computational and Clinical Applications in Abdominal Imaging, Held in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period9/18/119/18/11

Keywords

  • CAD
  • computer-aided detection
  • flat lesions
  • random forest
  • support vector machine
  • virtual colonoscopy
  • x-ray tomography

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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