Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine

Shuihua Wang; Yi Chen; Xing Xing Zhou; Jianfei Yang; Ling Wei; Ping Sun; Yudong Zhang

doi:10.1109/ISCID.2015.186

Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine

Shuihua Wang, Yi Chen^*, Xing Xing Zhou, Jianfei Yang, Ling Wei, Ping Sun, Yudong Zhang

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

6 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	409-412
Number of pages	4
ISBN (Electronic)	9781467395861
DOIs	https://doi.org/10.1109/ISCID.2015.186
Publication status	Published - 11 May 2016
Externally published	Yes
Event	8th International Symposium on Computational Intelligence and Design, ISCID 2015 - Hangzhou, Zhejiang, China Duration: 12 Dec 2015 → 13 Dec 2015

Publication series

Name	Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015
Volume	1

Conference

Conference	8th International Symposium on Computational Intelligence and Design, ISCID 2015
Country/Territory	China
City	Hangzhou, Zhejiang
Period	12/12/15 → 13/12/15

Keywords

Magnetic resonance imaging
fuzzy logic
fuzzy membership
fuzzy support vector machine
wavelet energy

Access to Document

10.1109/ISCID.2015.186

Cite this

Wang, S., Chen, Y., Zhou, X. X., Yang, J., Wei, L., Sun, P., & Zhang, Y. (2016). Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine. In Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015 (pp. 409-412). Article 7468980 (Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCID.2015.186

Wang, Shuihua ; Chen, Yi ; Zhou, Xing Xing et al. / Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine. Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 409-412 (Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015).

@inproceedings{9eb47aa3719e48a4864042ef01ae98b1,

title = "Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine",

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 = "Magnetic resonance imaging, fuzzy logic, fuzzy membership, fuzzy support vector machine, wavelet energy",

author = "Shuihua Wang and Yi Chen and Zhou, {Xing Xing} and Jianfei Yang and Ling Wei and Ping Sun and Yudong Zhang",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 8th International Symposium on Computational Intelligence and Design, ISCID 2015 ; Conference date: 12-12-2015 Through 13-12-2015",

year = "2016",

month = may,

day = "11",

doi = "10.1109/ISCID.2015.186",

language = "English",

series = "Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "409--412",

booktitle = "Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015",

}

Wang, S, Chen, Y, Zhou, XX, Yang, J, Wei, L, Sun, P & Zhang, Y 2016, Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine. in Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015., 7468980, Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015, vol. 1, Institute of Electrical and Electronics Engineers Inc., pp. 409-412, 8th International Symposium on Computational Intelligence and Design, ISCID 2015, Hangzhou, Zhejiang, China, 12/12/15. https://doi.org/10.1109/ISCID.2015.186

Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine. / Wang, Shuihua; Chen, Yi; Zhou, Xing Xing et al.
Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 409-412 7468980 (Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015; Vol. 1).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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T1 - Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine

AU - Wang, Shuihua

AU - Chen, Yi

AU - Zhou, Xing Xing

AU - Yang, Jianfei

AU - Wei, Ling

AU - Sun, Ping

AU - Zhang, Yudong

PY - 2016/5/11

Y1 - 2016/5/11

N2 - 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.

AB - 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.

KW - Magnetic resonance imaging

KW - fuzzy logic

KW - fuzzy membership

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KW - wavelet energy

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Wang S, Chen Y, Zhou XX, Yang J, Wei L, Sun P et al. Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine. In Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 409-412. 7468980. (Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015). doi: 10.1109/ISCID.2015.186

Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine

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