Probabilistic Neural Network Based Brain Tumor Detection and Classification System

Abstract

Our Goal is to increase the accuracy of brain tumor detection and classification and thereby replace conventional invasive and time consuming techniques. Here a new technique is proposed to classify the brain MRI images and to detect the brain tumor using probabilistic neural network. The proposed methodology comprises of three phases. 1) Discrete wavelet transform 2) Modified region growing algorithm and 3) Probabilistic neural network. Initially, the input is subjected to discrete wavelet transform. It is used to extract the wavelet coefficients from the MRI images. Then the texture features are extracted using modified region growing algorithm from the input MRI brain images, which are obtained from the database. The texture features taken in to consideration are correlation and contrast. Soon after, the extracted features are fed as the input to the Hybrid ANN-PNN to classify the brain MRI images. Based on the features extracted the tumor will be detected and will be classified as Benign and malignant tumor. The proposed methodology will be implemented in MATLAB 7.12 with different datasets. The performance will be analyzed with existing detection methods and we will prove our efficiency in terms of accuracy.