Each BUS image is fed into the trained generative network to obtain the boundary of the tumor, and then 13 statistical features from that boundary are computed: fractal dimension, lacunarity, convex hull, convexity, circularity, area, perimeter, centroid, minor and major axis length, smoothness, Hu moments (6) and central moments (order 3 and below). Exhaustive Feature Selection (EFS) algorithm is used to select the best set of features. The EFS algorithm indicates that the fractal dimension, lacunarity, convex hull, and centroid are the 4 optimal features. The selected features are fed into a Random Forest classifier, which is later trained to discriminate between benign and malignant tumors. Each BUS image is fed into the trained generative network to obtain the boundary of the tumor, and then 13 statistical features from that boundary are computed: fractal dimension, lacunarity, convex hull, convexity, circularity, area, perimeter, centroid, minor and major axis length, smoothness, Hu moments (6) and central moments (order 3 and below).