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Reseach Article

Design and Comparative Analysis of SVM and PR Neural Network for Classification of Brain Tumour in MRI

by Meenu Rohilla, Vinay Singal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 17
Year of Publication: 2014
Authors: Meenu Rohilla, Vinay Singal
10.5120/16101-5287

Meenu Rohilla, Vinay Singal . Design and Comparative Analysis of SVM and PR Neural Network for Classification of Brain Tumour in MRI. International Journal of Computer Applications. 91, 17 ( April 2014), 15-21. DOI=10.5120/16101-5287

@article{ 10.5120/16101-5287,
author = { Meenu Rohilla, Vinay Singal },
title = { Design and Comparative Analysis of SVM and PR Neural Network for Classification of Brain Tumour in MRI },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 17 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number17/16101-5287/ },
doi = { 10.5120/16101-5287 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:12:59.782003+05:30
%A Meenu Rohilla
%A Vinay Singal
%T Design and Comparative Analysis of SVM and PR Neural Network for Classification of Brain Tumour in MRI
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 17
%P 15-21
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electroencephalograms (EEGs) or MRI are progressively emerging as a significant measure of brain activity and they possess immense potential for the diagnosis and treatment of mental and brain diseases and abnormalities. This research paper presents an automated system for efficient classification of brain tumours in MRI images using Support Vector Machine (SVM). It works on the principle of a vector which supports in training of classifier using train images. The results obtained from the above method are compared with those obtained from Artificial Neural Networks (ANNs). This method uses pattern recognition algorithms for the classification of tumour in MR images. First, a pattern recognition neural network is created and then trained. The trained pattern recognition neural network when fed with a test MR image, effectively classifies the images. The performance analysis shows that qualitative results obtained from the proposed model are comparable with those obtained by ANN. The experimental results demonstrate the effectiveness of the proposed system in classification of brain tumour in MR images. The implementation is done using MATLAB R2012b using Artificial Neural Network and Image Processing Toolbox.

References
  1. M. Murugesan, R. Sukanesh , "Automated Detection of Brain Tumour in EEG Signals Using Artificial Neural Networks" 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.
  2. Satish Chandra, Rajesh Bhat, Harinder Singh, "A PSO Based method for Detection of Brain Tumours from MRI" 978-1-4244-5612-3/09/$26. 00_c 2009 IEEE.
  3. M. Usman Akram, Anam Usman, "Computer Aided System for Brain Tumour Detection and Segmentation" 978-1-61284-941-6/11/$26. 00 ©2011 IEEE.
  4. V. P. Gladis pushpa rathi, S. Palani, "Detection and Characterization of Brain Tumour Using Segmentation based on HSOM, Wavelet packet feature spaces and ANN" 978-1-4244-8679-3/11/$26. 00 ©2011 IEEE.
  5. J. selvakumar, A. Lakshmi, T. Arivoli, "Brain Tumour Segmentation and Its Area Calculation in Brain MR Images using K-Mean Clustering and Fuzzy C-Mean Algorithm" IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012) March 30, 31, 2012.
  6. Rupsa Bhattacharjee, Monisha Chakraborty, "Brain Tumour Detection From MR Images: Image Processing, Slicing and PCA Based Reconstruction" 2012 Third International Conference on Emerging Applications of Information Technology (EAIT).
  7. T. Uchiyama, K. Mohri, M. Shnkai, A. Ohshma, H. Honda, T. Kobayashi, T. Wakabayashi and J. Yoshida, "Position sensing of magnetic gel Using MI Sensor for Brain Tumour Detection" IEEE Transaction on magnetics, Vol. 33, No. 5, September 1997.
  8. C. C. Leung', W. F. Chen', P. C. K. Kwok3, and F. H. Y. Chan, "Brain Tumor Boundary Detection in MR Image with Generalized Fuzzy Operator"0-7803-7750-8/03/00 02003 IEEE.
  9. Yun Zhou, Sung-Cheng Huang, Shanglian Bao, Dean F. Wong, "Parametric Imaging and Statistical Mapping of Brain Tumor in Ga-68 EDTA Dynamic PET Studies" 0-7803-7324-3/02 © 2002 IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Tumour MRI Pattern recognition SVM Neural network.