Cells clonal selection for Breast Cancer classification

Author

Daoudi, Ryma ; Djemal, Khalifa ; Benyettou, Abdelkader

Author_Institution

IBISC Lab., Univ. of Evry Val d`Essonne, Evry, France

fYear

2013

fDate

18-21 March 2013

Firstpage

1

Lastpage

4

Abstract

In the last decade, several techniques of artificial intelligence proved their skills in the field of classification of cancer cells. We propose in this article a new idea of learning of the artificial immune systems (AIS) in the aim of improving CLONALG, one of the most popular algorithms in the field of the AIS. The principle of IMPROVED-CLONALG is to select the best cells to be cloned by calculating the averages of groups of the most competent cells in measures of similarity. The database used is Wisconsin Breast Cancer Database; promising results were found with compared to other implemented AIS algorithms.

Keywords

artificial immune systems; cancer; cellular biophysics; learning (artificial intelligence); medical diagnostic computing; pattern classification; AIS algorithms; IMPROVED-CLONALG principle; Wisconsin Breast Cancer Database; artificial immune systems; artificial intelligence; breast cancer classification; cells clonal selection; learning; Breast cancer; Classification algorithms; Cloning; Databases; Educational institutions; Immune system; Breast Cancer; Classification; Cloning; Memory Cells; Mutate;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Signals & Devices (SSD), 2013 10th International Multi-Conference on

Conference_Location

Hammamet

Print_ISBN

978-1-4673-6459-1

Electronic_ISBN

978-1-4673-6458-4

Type

conf

DOI

10.1109/SSD.2013.6564016

Filename

6564016