SVM-PUK Kernel Based MRI-brain Tumor Identification Using Texture and Gabor Wavelets | Zendy

Siva Koteswara Rao Chinnam | Zendy; Venkatramaphanikumar Sistla | Zendy; Venkata Krishna Kishore Kolli | Zendy

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SVM-PUK Kernel Based MRI-brain Tumor Identification Using Texture and Gabor Wavelets

Author(s) -

Siva Koteswara Rao Chinnam,

Venkatramaphanikumar Sistla,

Venkata Krishna Kishore Kolli

Publication year - 2019

Publication title -

traitement du signal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.279

H-Index - 11

eISSN - 1958-5608

pISSN - 0765-0019

DOI - 10.18280/ts.360209

Subject(s) - support vector machine , artificial intelligence , gabor wavelet , kernel (algebra) , pattern recognition (psychology) , texture (cosmology) , wavelet , computer vision , computer science , wavelet transform , mathematics , discrete wavelet transform , image (mathematics) , combinatorics

Received: 5 January 2019 Accepted: 16 March 2019 In this study, we propose an efficient method to identify unwanted growth in brain using SVMPUK on convoluted textural features with reduced Gabor wavelet features. After preprocessing, GLCM features of image are extracted and further, convoluted with reduced Gabor features using PCA of the image. Then, the convoluted GLCM features and reduced Gabor features classified with the SVM using PUK kernel. The proposed method performance is evaluated on BRATS’18 database and achieved an accuracy of 91.31 % in recognizing the effected tissues, and shown better performance over ED, DTW, FFNN and PNN.

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