Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection
Résumé
We present in this work an Artificial Immune System (AIS) algorithm for breast cancer classification and diagnosis. The main contribution is to select memory cells according to their belonging to a validity interval based on average similarity of training cells. The behaviour of these created memory cells preserves the diversity of original cancer learning class. All these operations allow to generate a set of memory cells with a global representativeness of the database which enables breast cancer classification and recognition. Promising results have been obtained on both Wisconsin Diagnosis Breast Cancer Database (WDBC) and (DDSM) Digital Database for Screening Mammography.