Brain Tumor Detection and Segmentaion in MR Images Using Kernel SVM

Abstract

Automatic detection of brain tumors and accurate classification of magnetic resonance image (MRI) of brain tumors has been a perplexed technique to obtain variability and complexity of the position, texture, size, and outlining of the lesions. In our method, we presented a novel approach to distinguish the normal and abnormal brain tumors in MR brain image. We implemented the technique over 200 brain MRIs in which 160 are abnormal and 40 are normal brain MRIs. Abnormal MRI is of two types cancerous or non- cancerous MRI. In this method first we applying some pre-processing technique and DWT technique after that we used the principal component analysis (PCA) followed by training and testing the brain MRIs using machine learning technique kernel support vector machine (KSVM). Kernel- SVM gives a good classification result in overall classification techniques of machine learning. We compares the linear and Non- linear and the RGB kernel support vector machines accuracy for classification of brain tumor.