Automated Classification of Mammographic Abnormalities Using Transductive Semi Supervised Learning Algorithm

Computer-aided diagnosis (CAD) of breast cancer is becoming a necessity given the exponential growth of performed. CAD are usually characterized by the large volume of acquired data that must be labeled in a specific way that leads to a major problem which is labeling operation. As a result the community of machine learning has attempted to respond to these practical needs by introducing the semi-supervised learning. The motivation of the current research is to propose a TSVM-CAD system for mammography abnormalities detection using a new Transductive TSVM with comparison of its kernel functions. The effectiveness of the system is examined on the Digital Database for Screening Mammography database DDSM using classification accuracy, sensitivity and specificity. Experimental results are very encouraging.