Classification of Brain MRI Tumor Images: A Hybrid Approach

Nowadays, brain tumor has been proved as a life threatening disease which cause even to death. Various classification techniques have been identified for Brain MRI Tumor Images. In this paper brain tumor from MR Images with the help of hybrid approach has been carried out. This hybrid approach includes discrete wavelet transform (DWT) to be used for extraction of features, Genetic algorithm for diminishing the number of features and support vector machine (SVM) for brain tumor classification. Images are downloaded from SICAS Medical Image Repository which classified images as benign or malign type. The proposed hybrid approach is implemented in MATLAB 2015a platform. Parameters used for analyzing the images are given as: entropy, smoothness, root mean square error (RMS), kurtosis and correlation. The simulation analysis approach results shows that hybrid approach offers better performance by improving accuracy and minimizing the RMS error in comparison with the state-of-the-art techniques in the similar context. (C) 2017 The Authors. Published by Elsevier B.V.