title: Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound.
abstract: An efficient classification algorithm is proposed for characterizing breast lesions. The algorithm is based on the cubic least squares mapping and the linear-kernel support vector machine (SVM(LSM)) classifier. Ultrasound images of 154 confirmed lesions (59 benign and 52 malignant solid masses, 7 simple cysts, and 32 complicated cysts) were manually segmented by a physician using a custom developed software. Texture and outline features and the SVM(LSM) algorithm were used to design a hierarchical tree classification system. Classification accuracy was 98.7%, misdiagnosing 1 malignant an 1 benign solid lesions only. This system may be used as a second opinion tool to the radiologists.
authors.affiliation: Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Ag. Spyridonos Street, Egaleo GR-122 10 Athens, Greece.
authors.forename: N
authors.initials: N
authors.lastname: Piliouras
authors.forename: I
authors.initials: I
authors.lastname: Kalatzis
authors.forename: N
authors.initials: N
authors.lastname: Dimitropoulos
authors.forename: D
authors.initials: D
authors.lastname: Cavouras
mesh_terms: D001941:Breast Diseases / Q000145:classification / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D003951:Diagnostic Errors; D005260:Female; D006115:Greece; D006801:Humans; D016018:Least-Squares Analysis; D014463:Ultrasonography