ISSN: 0973-7510

E-ISSN: 2581-690X

V. Ponniyin Selvan and M. Suganthi
1Department of Electronics and Communication Engineering, Mahendra College of Engineering, Salem, Tamilnadu, India.
2Department of Electronics and Communication Engineering, Mahendra College of Engineering, Salem, Tamilnadu, India.
J Pure Appl Microbiol. 2015;9(Spl. Edn. Aug.):253-261
© The Author(s). 2015
Received: 02/04/2015 | Accepted: 20/06/2015 | Published: 31/08/2015
Abstract

An improved Computer Aided Clinical Decision Making System for classifying the tumor has been developed and presented in this paper. The texture and shape features extracted from preprocessed mammograms have been utilized to obtain the optimal multiple  feature sets using multiobjective genetic algorithm. The Multilayer Back Propagation Neural Network (MBPN), Self Organising Map(SOM) with major voting method have been used to classify the tumor as benign or malignant. The multiple features with optimal feature selection is found to have the diagnostic accuracy 99.5%. The performance of the proposed clinical decision support system has been estimated and found that this system will provide valuable information to the physicians in clinical pathology.

Keywords

Mammogram, Image Denoising and Enhancement, Feature Extraction, Multilayer Back Propagation Network, Self Organising Map

Article Metrics

Article View: 0

Share This Article

© The Author(s) 2015. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.