Support vector machines in sonography: application to decision making in the diagnosis of breast cancer

We evaluated a series of pathologically proven breast tumors using the support vector machine (SVM) in the differential diagnosis of solid breast tumors. This study evaluated two ultrasonic image databases, i.e., DB1 and DB2. The DB1 contained 140 ultrasonic images of solid breast nodules (52 malignant and 88 benign). The DB2 contained 250 ultrasonic images of solid breast nodules (35 malignant and 215 benign). The physician-located regions of interest (ROI) of sonography and textual features were utilized to classify breast tumors. An SVM classifier using interpixel textual features classified the tumor as benign or malignant. The receiver operating characteristic (ROC) area index for the proposed system on the DB1 and the DB2 are 0.9695+/-0.0150 and 0.9552+/-0.0161, respectively. The proposed system differentiates solid breast nodules with a relatively high accuracy and helps inexperienced operators avoid misdiagnosis. The main advantage in the proposed system is that the training procedure of SVM was very fast and stable. The training and diagnosis procedure of the proposed system is almost 700 times faster than that of multilayer perception neural networks (MLPs). With the growth of the database, new ultrasonic images can be collected and used as reference cases while performing diagnoses. This study reduces the training and diagnosis time dramatically.