Computer Assisted Diagnosis of Tumor in Brain MRI Images using Wavelet as input to Ada-Boost classifier

Brain tumor segmentation is an significant method in medical image analysis since it provides an information related to anatomical structures as well as possible anomalous tissues necessary to treatment planning and patient follow-up. In this paper, fuly automatic brain tumor segmentation scheme is presented, which focuses on the structural analysis on both tumorous and normal tissues. Our proposed method consists of three major steps: i) tumor region location ii) feature extraction using wavelet iii) feature reduction using principe component analysis and iii) classification using Ada-Boost classifier . The experimental results are validated using the evaluation metrics such as, sensitivity, specificity, and accuracy. The authors proposed system is compared to other neural network classifier such as Feed Forward Neural Network(FFNN) and Radial Basics Function (RBF). The classification accuracy of the proposed system results is better compared to other leading methods.