NDLI: Performance Comparison of ESVM and CSVM for Classifying the Lung Nodules on CT Scans

Content Provider	IEEE Xplore Digital Library
Author	Jing Zhang Mao-Yong Cao Wen-dong Gai Bin Li
Copyright Year	2013
Description	Author affiliation: Sch. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China (Bin Li) \|\| Coll. of Inf. & Electr. Eng., Shandong Univ. of Sci. & Technol., Qingdao, China (Jing Zhang; Mao-Yong Cao; Wen-dong Gai)
Abstract	The nodules and the multiple times larger non-nodules of the regions of interested(ROIs) in lung areas are achieved, that would lead to a serious imbalance on the sample data. Many scholars have proposed some algorithms to solve this problem. In this paper, in order to guarantee that there is no correlation among the extracted characteristics, the PCA method is adopted to optimize and reduce dimensions, and then the modified support vector machine(SVM) classifiers using the sequential minimal optimization(SMO) algorithm and the grid research method are proposed to improve the computing efficiency. Furthermore, the abundant lung CT images from the hospital partnership could confirm the experimental results. We compare the classification performance between the ensemble SVM(ESVM) classifier and the cost-sensitive SVM(CSVM) classifier to deal with this problem. Experimental results show the performance of the CSVM classifier based on grid search is satisfactory than the ESVM.
Starting Page	409
Ending Page	413
File Size	616504
Page Count	5
File Format	PDF
ISBN	9780769550503
DOI	10.1109/ICIG.2013.87
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Sensitivity Lungs Computed tomography imbalanced dataset ESVM CSVM Educational institutions Classification algorithms classification lung nodule
Content Type	Text
Resource Type	Article

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