Paper Title
Brain Tumor Classification using Bat-Ann with BWT Segmentation Technique

Abstract
The segmentation and classification of infected tumor area from magnetic resonance imaging (MRI) are a major concern but a tedious and time consuming task achieved by medical specialists, and their accuracy depends on their knowledge only. Consequently, the utilization of computer aided technology becomes very essential to overcome these restrictions. In this paper, to increase the performance and diminish the difficulty includes in the image segmentation method, we have considered Berkeley wavelet transformation (BWT) based tumor segmentation. Moreover, to increase the accuracy and quality rate, a Bat algorithm based Artificial Neural Network (ANN) classifier by extracting relevant features from each segmented tissue. The results of proposed technique have been assessed and validated for performance on MRI images, based on accuracy, sensitivity, specificity, and so on. Keywords - MRI, BWT, BAT-ANN, Tumor.

Author - Deepa A. R, Sam Emmanuel W.R

Published : Volume-4,Issue-11  ( Nov, 2017 )

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