期刊
SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL
卷 92, 期 9, 页码 861-871出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0037549716666962
关键词
Multiple sclerosis; stationary wavelet entropy; decision tree; k-nearest neighbors; support vector machine; machine learning
资金
- Open Project Program of the State Key Lab of CADAMP
- CG, Zhejiang University [A1616]
- Open Fund of the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University [93K172016K17]
- Open Fund of the Key Laboratory of Statistical Information Technology and Data Mining, State Statistics Bureau [SDL201608]
- Natural Science Foundation of Jiangsu Province [BK20150983]
- Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology [2016WLZC013]
- NSFC fund [61602250]
- Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing [BD201607]
In order to detect multiple sclerosis (MS) subjects from healthy controls (HCs) in magnetic resonance imaging, we developed a new system based on machine learning. The MS imaging data was downloaded from the eHealth laboratory at the University of Cyprus, and the HC imaging data was scanned in our local hospital with volunteers enrolled from community advertisement. Inter-scan normalization was employed to remove the gray-level difference. We adjust the misclassification costs to alleviate the effect of unbalanced class distribution to the classification performance. We utilized two-level stationary wavelet entropy (SWE) to extract features from brain images. Then, we compared three machine learning based classifiers: the decision tree, k-nearest neighbors (kNN), and support vector machine. The experimental results showed the kNN performed the best among all three classifiers. In addition, the proposed SWE+kNN approach is superior to four state-of-the-art approaches. Our proposed MS detection approach is effective.
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