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

Authors:
Janne J. Näppi;Daniele Regge;Hiroyuki Yoshida
Affiliations:
Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA;Institute for Cancer Research and Treatment, Turin, Italy;Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
Venue:
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
Year:
2011

Citing 2
Cited 1

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Advances in kernel methods: support vector learning
Random Forests

Machine Learning

Adaptive volumetric detection of lesions for minimal-preparation dual-energy CT colonography

MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications

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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.