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

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

A. Jayachandran, R. Dhanasekaran
Copyright: © 2014 |Volume: 3 |Issue: 3 |Pages: 14
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781466654099|DOI: 10.4018/ijeoe.2014070105
Cite Article Cite Article

MLA

Jayachandran, A., and R. Dhanasekaran. "Computer Assisted Diagnosis of Tumor in Brain MRI Images using Wavelet as input to Ada-Boost classifier." IJEOE vol.3, no.3 2014: pp.72-85. http://doi.org/10.4018/ijeoe.2014070105

APA

Jayachandran, A. & Dhanasekaran, R. (2014). Computer Assisted Diagnosis of Tumor in Brain MRI Images using Wavelet as input to Ada-Boost classifier. International Journal of Energy Optimization and Engineering (IJEOE), 3(3), 72-85. http://doi.org/10.4018/ijeoe.2014070105

Chicago

Jayachandran, A., and R. Dhanasekaran. "Computer Assisted Diagnosis of Tumor in Brain MRI Images using Wavelet as input to Ada-Boost classifier," International Journal of Energy Optimization and Engineering (IJEOE) 3, no.3: 72-85. http://doi.org/10.4018/ijeoe.2014070105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.