Classifying Mammography Images by Using Fuzzy Cognitive Maps and a New Segmentation Algorithm

Mammography is one of the best techniques for the early detection of breast cancer. In this chapter, a method based on fuzzy cognitive map (FCM) and its evolutionary-based learning capabilities is presented for classifying mammography images. The main contribution of this work is two-fold: (a) to propose a new segmentation approach called the threshold based region growing (TBRG) algorithm for segmentation of mammography images, and (b) to implement FCM method in the context of mammography image classification by developing a new FCM learning algorithm efficient for tumor classification. By applying the proposed (TBRG) algorithm, a possible tumor is delineated against the background tissue. We extracted 36 features from the tissue, describing the texture and the boundary of the segmented region. Due to the curse of dimensionality of features space, the features were selected with the help of the continuous particle swarm optimization algorithm. The FCM was trained using a new evolutionary approach based on the area under curve (AUC) of the output concept. In order to evaluate the efficacy of the presented scheme, comparisons with benchmark machine learning algorithms were conducted and known metrics like ROC, AUC were calculated. The AUC obtained for the test data set is 87.11%, which indicates the excellent performance of the proposed FCM.