earticle

영어

Breast cancer is reported as the second most deadly cancer in the world and the main of mortality among the women, on which public awareness has been increasing during the last few decades. This is why several works are made to develop help tools for disease diagnosis. Computer-Assisted Diagnosis (CAD) is based on 3 main steps: segmentation, feature extraction and classification in order to generate a final decision. Classification phase is the key step in this process; for that, many research have been accentuated in this domain and many techniques were be proposed. Kernel combination is a current active topic in the field of machine learning. It takes benefit of classifier algorithms. it allows to choose the kernel functions according to the features vectors. The combination of Kernel-based classifiers was proposed as a research way allowing reliability recognition by using the complementarily which can exist between classifiers. This study investigated a computer-aided diagnosis system for breast cancer by developing a novel classifier fusion scheme based on fusion of three support vector machine classifier. Each one is associated with an homogenous family of features (Hu moments; central moments, Haralick moment) as efficient learning algorithm and diversity between features family as fusion criteria to ensure best performance. Our experiments demonstrated that developed system using Database for Screening Mammography (DDSM) database achieve very encouraging results when compared with past works using the same information.