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.