NOVEL SCHEME OF FEATURES EXTRACTION & CLASSIFICATION OF BRAIN TUMOR INFECTED MRI IMAGE USING NEURAL NETWORK

Abstract

Magnetic resonance (MR) imaging has been playing an important role in neuroscience research for studying brain images where MR’s soft tissue contrast and non invasiveness are clear advantages. MR images can also be used to determine normal and abnormal types of brain. Moreover, the MRI characteristics will help the doctor to avoid the human error in manual interpretation of medical content. Computer-based classification has remained largely experimental work with approaches. Here a work is done by simulating a method in Matlab using artificial neural network to automatically classify brain MRI images. The diagnosis method consists of three stages firstly feature extraction using discrete wavelet transforms. Wavelets seem to be a suitable tool for this task, because they allow analysis of images at various levels of resolution. Then the features are reduced using principal component analysis (PCA). In the last stage artificial neural network (ANN) is used as a multi class classification technique to classify between normal & brain tumor infected MRI Images & also classify different brain tumor images according to the different location of Tumor in the brain. We obtain good classification rate with the less number of features. The results show that the method is robust and effective.