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Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 368674, 13 pages
http://dx.doi.org/10.1155/2015/368674
Research Article

Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE

1Software College, Northeastern University, Shenyang 110004, China
2School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
3Key Laboratory of Medical Imaging Calculation of the Ministry of Education, Shenyang 110004, China

Received 6 January 2015; Revised 9 March 2015; Accepted 14 March 2015

Academic Editor: Giancarlo Ferrigno

Copyright © 2015 Yuan Sui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In lung cancer computer-aided detection/diagnosis (CAD) systems, classification of regions of interest (ROI) is often used to detect/diagnose lung nodule accurately. However, problems of unbalanced datasets often have detrimental effects on the performance of classification. In this paper, both minority and majority classes are resampled to increase the generalization ability. We propose a novel SVM classifier combined with random undersampling (RU) and SMOTE for lung nodule recognition. The combinations of the two resampling methods not only achieve a balanced training samples but also remove noise and duplicate information in the training sample and retain useful information to improve the effective data utilization, hence improving performance of SVM algorithm for pulmonary nodules classification under the unbalanced data. Eight features including 2D and 3D features are extracted for training and classification. Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.