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Breast cancer ranks first in the causes of cancer deaths among women around the world. Early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Mammography is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In this aim, Digital Database for Screening Mammography (DDSM) is an invaluable resource for digital mammography research, the purpose of this resource is to provide a large set of mammograms in a digital format. DDSM has been widely used by researchers to evaluate different computer-aided algorithms such as neural networks or SVM. The Artificial Immune Systems (AIS) are adaptive systems inspired by the biological immune system, they are able of learning, memorize and perform pattern recognition. We propose in this paper several enhancements of CLONALG algorithm, one of the most popular algorithms in the AIS field, which are applied on DDSM for breast cancer classification using adapted descriptors. The obtained classification results are 98.31% for CCS-AIS and 97.74% for MFAIS against 95.57% for original CLONALG. This proves the effectiveness of the used descriptors in the two improved techniques.

Digital Database for Screening Mammography Classification Using Improved Artificial Immune System Approaches