CAD system for classification of mammographic abnormalities using transductive semi supervised learning algorithm and heterogeneous features

Abstract

Breast cancer is the most frequently diagnosed cancer in women worldwide and the leading cause of cancer death among females. Currently the most effective method for early detection and screening of breast abnormalities is mammography. In particular, the breast mass diagnosis and classification arouse nowadays a great interest. Various researches have proven that the computer-aided diagnosis (CAD) of breast abnormalities is becoming increasingly a necessity given the exponential growth of performed. Hence, it can reduce the cost for double screening process A generic CAD system includes segmentation, feature extraction, and classification stages in order to have a final decision. However, such a system is usually characterized by the large volume of the acquired data. This data must be labeled in a specific way that leads to a major problem which is the necessity of an expert to make the labeling operation. To overcome this constraint, statistical learning propose semi-supervised learning (SSL) algorithm as alternative in order to beneficiate to the all dataset images. In this paper, a CAD system for the breast abnormalities classification is proposed basing on Transductive semi-supervised learning technique using TSVM with these different kernel functions and heterogeneous features families. Experimental results based on DDSM dataset are very encouraging.