期刊
MULTIMEDIA TOOLS AND APPLICATIONS
卷 75, 期 23, 页码 15601-15617出版社
SPRINGER
DOI: 10.1007/s11042-015-2649-7
关键词
Classification; Pattern recognition; Support vector machine; Magnetic resonance imaging; Biogeography-based optimization
类别
资金
- NSFC [610011024, 61273243, 51407095]
- Program of Natural Science Research of Jiangsu Higher Education Institutions [13KJB460011, 14KJB520021]
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing [BM2013006]
- Key Supporting Science and Technology Program (Industry) of Jiangsu Province [BE2012201, BE2014009-3, BE2013012-2]
- Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province [BA2013058]
- Nanjing Normal University Research Foundation for Talented Scholars [2013119XGQ0061, 2014119XGQ0080]
- Science Research Foundation of Hunan Provincial Education Department [12B023]
It is very important to early detect abnormal brains, in order to save social and hospital resources. The wavelet-energy was a successful feature descriptor that achieved excellent performances in various applications; hence, we proposed a novel wavelet-energy based approach for automated classification of MR brain images as normal or abnormal. SVM was used as the classifier, and biogeography-based optimization (BBO) was introduced to optimize the weights of the SVM. The results based on a 5 x 5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO-KSVM in terms of sensitivity and accuracy. The study offered a new means to detect abnormal brains with excellent performance.
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