Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images

To increase the ability of ultrasonographic (US) technology for the differe ntial diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using data mining with decision tree for classifica tion of breast tumor to increase the levels of diagnostic confidence and to provide the immediate second opinion for physicians. Cooperating with the texture information extracted from the region of interest (ROI) image, a de cision tree model generated from the training data in a top-down, general-t o-specific direction with 24 co-variance texture features is used to classi fy the tumors as benign or malignant. In the experiments, accuracy rates fo r a experienced physician and the proposed CADx are 86.67% (78/90) and 95.5 0% (86/90), respectively.