Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound.
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.
Piliouras
N
N
Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Ag. Spyridonos Street, Egaleo GR-122 10 Athens, Greece.
Kalatzis
I
I
Dimitropoulos
N
N
Cavouras
D
D
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