Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images
Most thyroid nodules are heterogeneous with various internal components, which confuse many radiologists and physicians with their various echo patterns in ultrasound images. Numerous textural feature extraction methods are used to characterize these patterns to reduce the misdiagnosis rate. Thyroid nodules can be classified using the corresponding textural features. In this paper, six support vector machines (SVMs) are adopted to select significant textural features and to classify the nodular lesions of a thyroid. Experiment results show that the proposed method can correctly and efficiently classify thyroid nodules. A comparison with existing methods shows that the feature-selection capability of the proposed method is similar to that of the sequential-floating-forward-selection (SFFS) method, while the execution time is about 337 times faster. In addition, the proposed criterion function achieves higher accuracy than those of the F-score, T-test, entropy, and Bhattacharyya distance methods.
feature selection support vector machines thyroid nodule classification