Neural networks and SMO based classification for brain tumor

In this model, we exploit the use of Sequential Minimal Optimization (SMO) to automatically classify brain MRI images either normal or abnormal for tumour. Based on symmetry of brain image, exhibited in the axial and coronal images, it is classified. Using the optimal texture features extracted from normal and tumor regions of MRI by using gray level co-occurrence matrix, SMO classifiers are used to classify and segment the tumor portion in abnormal images. Both the testing and training phase gives the percentage of accuracy on each parameter in SMO, which gives the idea to choose the best one to be used in further works. The results showed outperformance of SMO algorithm when compared to back propagation network with classification accuracy of 88.33% using radial basis function for better convergence and classification.

[1]  Hyeran Byun,et al.  Applications of Support Vector Machines for Pattern Recognition: A Survey , 2002, SVM.

[2]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[3]  Nishchal K. Verma,et al.  SVM based methods for arrhythmia classification in ECG , 2010, 2010 International Conference on Computer and Communication Technology (ICCCT).

[4]  Manik Varma,et al.  More generality in efficient multiple kernel learning , 2009, ICML '09.

[5]  Lalit M. Patnaik,et al.  Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network , 2006, Biomed. Signal Process. Control..

[6]  Chih-Jen Lin,et al.  Asymptotic convergence of an SMO algorithm without any assumptions , 2002, IEEE Trans. Neural Networks.

[7]  Ron Kikinis,et al.  Automated Segmentation of MRI of Brain Tumors , 2001 .

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Jian-Hong Chen,et al.  Extension Neural Network Approach to Classification of Brain MRI , 2009, 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[10]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[11]  Johan Montagnat,et al.  Automated Estimation of Brain Volume in Multiple Sclerosis with BICCR , 2001, IPMI.

[12]  S. Ramakrishnan.  On the Application of Various Probabilistic Neural Networks in Solving Different Pattern Classification Problems , 2008 .

[13]  Brian C. Lovell,et al.  Classification of cervical cell nuclei using morphological segmentation and textural feature extraction , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[14]  Ghazanfar Latif,et al.  Classification and segmentation of brain tumor using texture analysis , 2010 .

[15]  Chin-Teng Lin,et al.  Support-vector-based fuzzy neural network for pattern classification , 2006, IEEE Transactions on Fuzzy Systems.

[16]  Federico Girosi,et al.  An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[17]  Ron Kikinis,et al.  Adaptive, template moderated, spatially varying statistical classification , 2000, Medical Image Anal..

[18]  Chih-Jen Lin.  Linear Convergence of a Decomposition Method for Support Vector Machines , 2001 .

[19]  Abdel-Badeeh M. Salem,et al.  Hybrid intelligent techniques for MRI brain images classification , 2010, Digit. Signal Process..

[20]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.

[21]  Z. Měř́ınský,et al.  Brain Tumour Diagnostic Support Based on Medical Image Segmentation , 2009 .

[22]  Jie Yang,et al.  Degree prediction of malignancy in brain glioma using support vector machines , 2006, Comput. Biol. Medicine.