DocumentCode :
3773502
Title :
Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine
Author :
Shuihua Wang;Yi Chen;Xing-Xing Zhou;Jianfei Yang;Ling Wei;Ping Sun;Yudong Zhang
Author_Institution :
Sch. of Comput. Sci. &
Volume :
1
fYear :
2015
Firstpage :
409
Lastpage :
412
Abstract :
It is important to early detect pathological brains. Traditional methods used plain support vector machine (SVM) that is vulnerable to noises and outliers. In this study, we presented a hybrid method that combined wavelet-energy (WE) and fuzzy support vector machine (FSVM). The results over a 5x5-fold cross validation showed that the proposed "WE + FSVM" produced accuracy of 93.78%, higher than "WE + KSVM" of 91.78%, "DWT + PCA + RBF-NN" of 91.33%, "WE + BP-NN" of 86.67%, and "DWT + PCA + BP-NN" of 86.22%. Therefore, this study offered a new means to solve the problem with excellent performance.
Keywords :
"Discrete wavelet transforms","Support vector machines","Pathology","Principal component analysis","Feature extraction","Diseases","Artificial neural networks"
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Design (ISCID), 2015 8th International Symposium on
Print_ISBN :
978-1-4673-9586-1
Type :
conf
DOI :
10.1109/ISCID.2015.186
Filename :
7468980
Link To Document :
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