alexa The Neural Networks with an Incremental Learning Algorithm Approach for Mass Classification in Breast Cancer
ISSN: 2090-4924

International Journal of Biomedical Data Mining
Open Access

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Review Article

The Neural Networks with an Incremental Learning Algorithm Approach for Mass Classification in Breast Cancer

Zribi M* and Boujelbene Y

Faculty of Economic Science and Management, Sfax University, Tunisia

Corresponding Author:
Manel Zribi
Faculty of Economic Science and Management
Sfax University, Tunisia
Tel: +216 74 242 951
E-mail: manel0306@yahoo.fr

Received date: February 22, 2016; Accepted date: March 29, 2016; Published date: March 31, 2016

Citation: Zribi M, Boujelbene Y (2016) The Neural Networks with an Incremental Learning Algorithm Approach for Mass Classification in Breast Cancer. Int J Biomed Data Min 5:118. doi: 10.4172/2090-4924.1000118

Copyright: © 2016 Manel Zribi, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

As breast cancer can be very aggressive, only early detection can prevent mortality. The proposed system is to eliminate the unnecessary waiting time as well as reducing human and technical errors in diagnosing breast cancer. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper uses the neural networks with an incremental learning algorithm as a tool to classify a mass in the breast (benign and malignant) using selection of the most relevant risk factors and decision making of the breast cancer diagnosis To test the proposed algorithm we used the Wisconsin Breast Cancer Database (WBCD). ANN with an incremental learning algorithm performance is tested using classification accuracy, sensitivity and specificity analysis, and confusion matrix. The obtained classification accuracy of 99.95%, a very promising result compared with previous algorithms already applied and recent classification techniques applied to the same database.

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