MRI Brain Tumor Classification Technique Using Fuzzy C-Means Clustering and Artificial Neural Network

Abstract

Brain tumor is a deathly disease and it is indispensable to point out the tumor immediately. Detection of brain tumor from MRI with higher accuracy has become a major research region for the medical sector. In this paper, an automatic brain tumor classification procedure applying fuzzy C-means and artificial neural network is proposed which provides higher precision. For this proposed technique, inputted MRI images are resized and then sharpening filter is used for preprocessing. After that, fuzzy C-means cluster process is chosen for image segmentation. At the next step, discreate wavelet transform is utilized for feature extraction and then features quantity are reduced by principal component analysis. Furthermore, reduced features are taken to artificial neural network for brain tumor classification. An effective training function Levenberg–Marquardt is used for neural network. This proposed method provides 99.8% accuracy, 100% sensitivity and 99.59% specificity which is comparatively better than other existing detection techniques.