Improving cells recognition by local database categorization in Artificial Immune System algorithm. Application to breast cancer diagnosis

In this work, a hybrid classification system based local database categorization is proposed for breast cancer classification. The proposed approach aims to improve the classification rate of the Artificial Immune System (AIS) and reduce its computational time. The principle of the hybrid classifier based AIS consists in categorizing the cells sets in multiple local clusters using k-means algorithm and learning each cluster by the Radial Basis Function Neural Network. The goal of the categorization of data is to reduce the number of tests performed by each training example in AIS algorithms to select the nearest cell to be cloned which improves the cells recognition. The results obtained on the Digital Database for Screening Mammography (DDSM) show the effectiveness of the proposed classifier either in classification accuracy or computing costs compared to other AIS algorithms.