It is a difficult and complex task for a radiologist or clinical practitioner to segment, detect, and extract infected tumor area and classify the type of tumor from magnetic resonance (MR) images. This paper presents a PSO (Particle Swarm Optimization) based LLRBFNN (Local Linear Radial Basis Function Neural Network) model to classify and detect brain tumors into malignant (cancerous) and benign (noncancerous). In this paper we have used wavelet transform to improve the performance of MR image segmentation process and feature extraction. For the validation of the proposed PSO based LLRBFNN model, the machine learning approach support vector machine (SVM) and LMS (Least Mean Square) based LLRBFNN classifier also investigated. The research work follows the steps such as feature extraction out of which relevant features are considered for the research work. In the second step the features are fed as input to the proposed PSO based LLRBFNN Model for the classification task. In the third step the machine learning approach SVM and LMS based LLRBFNN has been applied for classification task and the results are compared. It is found that the proposed model takes less computational time than the SVM and LMS based LLRBFNN machine learning approach. In contrast to classification results the proposed model gives better classification results. Based on accuracy it is also noticed that the proposed model shows better performance in accuracy and quality analysis on MRI brain images.

SVM, Wavelet Transform, DWT, PSO, LLRBFNN, Brain tumour, Feature extraction

[1] Pauline John,Brain Tumor, “Classification Using Wavelet and Texture Based Neural Network”, International Journal of Scientific & Engineering Research Vol. 3, Issue 10, October-2012 1 ISSN 2229-5518.
[2] Carlos Arizmendi, Alfredo Vellido, Enrique Romero, “Binary Classification of Binary Tumors using a Discrete Wavelet Transform and Energy Criteria”, Second Latin American Symposium on Circuits and Systems (LASCAS), IEEE, pp.1-4, 2011
[3] Adrian Ion-Margineanu, Sofie Van Cauter, Diana M Sima, Frederik Maes, Stefaan W. Van Gool, Stefan Sunaert, Uwe Himmelreich and Sabine Van Huffel, “ Tumour Relapse Prediction Using Multiparametric MR Data Recorded during Follow-Up of GBM Patients”, BioMed Research International, Article ID 842923, in press
[4] Kailash D.Kharat1, Pradyumna P.Kulkarni, M.B.Nagori , “Brain Tumor Classification Using Neural Network Based Methods”, International Journal of Computer Science and Informatics ISSN (PRINT): 2231 –5292, Vol-1, Issue-4, 2012
[5] Mohd Fauzi Bin Othman, Noramalina Bt Abdullah, “MRI Brain Classification using Support Vector Machine,” IEEE, 2011
[6] Mohd Fauzi Othman and Mohd Ariffanan Mohd Basri, “Probabilistic Neural Network for Brain Tumor Classification,” Second International Conference on Intelligent Systems, Modelling and Simulation. IEEE, ISMS.2011.32, 2011.
[7] S. Damodharan and D. Raghavan, “Combining tissue segmentation and neural network for brain tumor detection,” International Arab Journal of Information Technology, vol. 12, no. 1, pp. 42–52, 2015.
[8] M. Alfonse and A.-B. M. Salem, “An automatic classification of brain tumors through MRI using support vector machine,” Egyptian Computer Science Journal, Vol. 40, pp. 11–21, 2016.
[9] Q. Ain, M. A. Jaffar, and T.-S. Choi, “Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor,” Applied Soft Computing Journal, vol. 21, pp. 330– 340, 2014.
[10] E. Abdel-Maksoud, M. Elmogy, and R. Al-Awadi, “Brain tumor segmentation based on a hybrid clustering technique,” Egyptian Informatics Journal, vol. 16, no. 1, pp. 71–81, 2014.
[11] E. A. Zanaty, “Determination of gray matter (GM) and white matter (WM) volume in brain magnetic resonance images (MRI),” International Journal of Computer Applications, vol. 45, pp. 16–22, 2012
[12] T. Torheim, E. Malinen, K. Kvaal et al., “Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines,” IEEE Transactions on Medical Imaging, vol. 33, no. 8, pp. 1648–1656, 2014.
[13] J. Yao, J. Chen, and C. Chow, “Breast tumor analysis in dynamic contrast enhanced MRI using texture features and wavelet transform,” IEEE Journal on Selected Topics in Signal Processing, vol. 3, no. 1, pp. 94–100, 2009.
[14] P. Kumar and B. Vijayakumar, “Brain tumour Mr image segmentation and classification using by PCA and RBF kernel based support vector machine,” Middle-East Journal of Scientific Research, vol. 23, no. 9, pp. 2106–2116, 2015.
[15] W. Cui, Y. Wang, Y. Fan, Y. Feng, and T. Lei, “Localized FCM clustering with spatial information for medical image segmentation and bias field estimation,” International Journal of Biomedical Imaging, vol. 2013, Article ID 930301, 8 pages, 2013.
[16] G. Wang, J. Xu, Q. Dong, and Z. Pan, “Active contour model coupling with higher order diffusion for medical image segmentation,” International Journal of Biomedical Imaging, vol. 2014, Article ID 237648, 8 pages, 2014.
[17] A. Chaddad, “Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models,” International Journal of Biomedical Imaging, vol. 2015, Article ID 868031, 11 pages, 2015.
[18] S. N. Deepa and B. Arunadevi, “Extreme learning machine for classification of brain tumor in 3D MR images,” Informatologia, vol. 46, no. 2, pp. 111–121, 2013.
[19] B. Willmore, R. J. Prenger, M. C. Wu, and J. L. Gallant, “The Berkeley wavelet transform: a biologically inspired orthogonal wavelet transform,” Neural Computation, vol. 20, no. 6, pp. 1537– 1564, 2008.
[20] P. Remya Ravindran and K. P. Soman, “Berkeley wavelet transform based image watermarking,” in Proceedings of the International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom ’09), pp. 357–359, IEEE, Kerala, India, October 2009.
[21] C.H. Lin and C.H. Wang, “Adaptive Wavelet Networks for Power-Quality Detection and Discrimination in a Power System”, IEEE Transactions on Power Delivery, Vol.21, No.3, July 2006.
[22] P. K. Nayak, S. Mishra, P. K. Dash, Ranjeeta Bisoi, “Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances”, Neural Computing & Application,Springer,2016. DOI 10.1007/s00521-015-2010-0
[23] T. McConaghy, H. Lung, E. Bosse, V. Vardan, “Classification Of Audio Radar Signals Using Radial Basis Function Neural Network”, IEEE Transactions on Inst .And Measurement, Vol. 52, No.6, pp.1771-17779, Dec. 2003
[24] Z. L. Gaing, “Wavelet-Based Neural Network For Power Disturbance Recognition And Classification”, IEEE Trans. Power Deilivery, Vol.19, No.4, pp.1560–1568, Oct 2004.
[25] N. Najkar, F. Razzazi, H. Sameti. “A Novel Approach To HMM-based Speech Recognition Systems Using Particle Swarm Optimization”, Elsevier Science, Mathematical And Computer Modeling, Vol.52, No.11, pp.2157-2165, Dec.2010