Classification of the thyroid nodules using support vector machines

Abstract

Most of the thyroid nodules are heterogeneous with various internal components, which confuse many radiologists and physicians with their various echo patterns in thyroid nodules. A lot of texture extraction methods were used to characterize the thyroid nodules. Accordingly, the thyroid nodules could be classified by the corresponding textural features. In this paper, five support vector machines (SVM) were adopted to select the significant textural features and to classify the nodular lesions of thyroid. Experimental results showed the proposed method classifies the thyroid nodules correctly and efficiently. The comparison results demonstrated that the capability of feature selection of the proposed method was similar to the sequential floating forward selection (SFFS) method. However, the proposed method is faster than the SFFS method.