Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound
✍ Scribed by N. Piliouras; I. Kalatzis; N. Dimitropoulos; D. Cavouras
- Publisher
- Elsevier Science
- Year
- 2004
- Tongue
- English
- Weight
- 286 KB
- Volume
- 28
- Category
- Article
- ISSN
- 0895-6111
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✦ Synopsis
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.