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Proceedings Paper

An integrated classifier for computer-aided diagnosis of colorectal polyps based on random forest and location index strategies
Author(s): Yifan Hu; Hao Han; Wei Zhu; Lihong Li; Perry J. Pickhardt; Zhengrong Liang
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Paper Abstract

Feature classification plays an important role in differentiation or computer-aided diagnosis (CADx) of suspicious lesions. As a widely used ensemble learning algorithm for classification, random forest (RF) has a distinguished performance for CADx. Our recent study has shown that the location index (LI), which is derived from the well-known kNN (k nearest neighbor) and wkNN (weighted k nearest neighbor) classifier [1], has also a distinguished role in the classification for CADx. Therefore, in this paper, based on the property that the LI will achieve a very high accuracy, we design an algorithm to integrate the LI into RF for improved or higher value of AUC (area under the curve of receiver operating characteristics -- ROC). Experiments were performed by the use of a database of 153 lesions (polyps), including 116 neoplastic lesions and 37 hyperplastic lesions, with comparison to the existing classifiers of RF and wkNN, respectively. A noticeable gain by the proposed integrated classifier was quantified by the AUC measure.

Paper Details

Date Published: 24 March 2016
PDF: 8 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851L (24 March 2016); doi: 10.1117/12.2216353
Show Author Affiliations
Yifan Hu, Stony Brook Univ. (United States)
Hao Han, Stony Brook Univ. (United States)
Wei Zhu, Stony Brook Univ. (United States)
Lihong Li, College of Staten Island (United States)
Perry J. Pickhardt, Univ. of Wisconsin-Madison (United States)
Zhengrong Liang, Stony Brook Univ. (United States)

Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

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