Comparison of ANFIS and SVM for the classification of brain MRI Pathologies

Author

Lahmiri, Salim ; Boukadoum, Mounir

Author_Institution

Dept. of Comput. Sci., Univ. of Quebec at Montreal, Montreal, QC, Canada

fYear

2011

fDate

7-10 Aug. 2011

Firstpage

1

Lastpage

4

Abstract

The Adaptive Neuro-Fuzzy Inference System (ANFIS) and support vector machines (SVM) are compared in terms of pathologies detection in brain magnetic resonance images (MRI). Twelve features are extracted from LH and HL sub-bands of the two dimensional discrete wavelet transform (2D-DWT) using first order statistics; then, principal component analysis is employed to retain the six most significant characteristics. The simulation results show strong evidence of the superiority of SVM over ANFIS.

Keywords

biomedical MRI; brain; discrete wavelet transforms; fuzzy neural nets; medical image processing; principal component analysis; support vector machines; ANFIS; SVM; adaptive neuro-fuzzy inference system; brain MRI pathologies; brain magnetic resonance images; first order statistics; pathologies detection; principal component analysis; support vector machines; two dimensional discrete wavelet transform; Discrete wavelet transforms; Europe; Feature extraction; Resonant frequency; Sensitivity; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems (MWSCAS), 2011 IEEE 54th International Midwest Symposium on

Conference_Location

Seoul

ISSN

1548-3746

Print_ISBN

978-1-61284-856-3

Electronic_ISBN

1548-3746

Type

conf

DOI

10.1109/MWSCAS.2011.6026437

Filename

6026437