Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 
Year : 2018  |  Volume : 14  |  Issue : 3  |  Page : 625-633

Breast cancer tumor type recognition using graph feature selection technique and radial basis function neural network with optimal structure

1 Department of Electrical and Electronic Engineering, Eastern Mediterranean University, KKTC, Via Mersin-10, Gazimağusa, Turkey
2 Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran

Correspondence Address:
Dr. Abdoljalil Addeh
Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0973-1482.183561

Rights and Permissions

Context: Breast cancer is a major cause of mortality in young women in the developing countries. Early diagnosis is the key to improve survival rate in cancer patients. Aims: In this paper an intelligent system is proposed to breast cancer tumor type recognition. Settings and Design: The proposed system includes three main module: The feature selection module, the classifier module and the optimization module. Feature selection plays an important role in pattern recognition systems. The better selection of features usually results in higher accuracy rate. Methods and Material: In the proposed system we used a new graph based feature selection approach to select the best features. In the classifier module, the radial basis function neural network (RBFNN)is used as classifier. In RBF training, the number of RBFs and their respective centers and widths (Spread) have very important role in its performance. Therefore, artificial bee colony (ABC) algorithm is proposed for selecting appropriate parameters of the classifier. Statistical Analysis Used: The RBFNN with optimal structure and the selected feature classified the tumors with 99.59% accuracy. Results: The proposed system is tested on Wisconsin breast cancer database (WBCD) and the simulation results show that the recommended system exhibits a high accuracy. Conclusions: The proposed system has a high recognition accuracy and therefore we recommend the proposed system for breast cancer tumor type recognition.

Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)

 Article Access Statistics
    PDF Downloaded147    
    Comments [Add]    

Recommend this journal