Cells clonal selection for Breast Cancer classification

Rima Daoudi (1) , Khalifa Djemal (1) , Abdelkader Benyettou (2)

1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
2 Laboratoire Signal, Image et Parole

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.

Mots clés: Breast Cancer; Classification; Cloning; Memory Cells; Mutate

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Rima Daoudi, Khalifa Djemal, Abdelkader Benyettou. Cells clonal selection for Breast Cancer classification. 10th International Multi-Conference on Systems, Signals and Devices (SSD 2013), Mar 2013, Hammamet, Tunisia. (elec. proc), ⟨10.1109/SSD.2013.6564016⟩. ⟨hal-00860883⟩