DOIONLINE

DOIONLINE NO - IJAECS-IRAJ-DOIONLINE-10041

Publish In
International Journal of Advances in Electronics and Computer Science-IJAECS
Journal Home
Volume Issue
Issue
Volume-4,Issue-11  ( Nov, 2017 )
Paper Title
Brain Tumor Classification using Bat-Ann with BWT Segmentation Technique
Author Name
Deepa A. R, Sam Emmanuel W.R
Affilition
Department of Computer Science, Loyola Institute of Technology and science , Thovalai,India. Depatment of computer Science, Nesamony Christian College , Marthandam ,India.
Pages
117-120
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.
  View Paper