Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images

2010 ◽  
Vol 43 (10) ◽  
pp. 3494-3506 ◽  
Author(s):  
Chuan-Yu Chang ◽  
Shao-Jer Chen ◽  
Ming-Fong Tsai
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Thyroid Nodules ◽  
Support Vector ◽  
Ultrasound Images

scholarly journals Detection and classification of thyroid nodule using Shearlet coefficients and support vector machine

2017 ◽  
Vol 6 (3) ◽  
pp. 50
Author(s):  
Nanda S. ◽  
Sukumar M.
Keyword(s):  
Support Vector Machine ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Textural Features ◽  
Ultrasound Images ◽  
K Nearest Neighbor ◽  
Benign Nodules

Thyroid nodules have diversified internal components and dissimilar echo patterns in ultrasound images. Textural features are used to characterize these echo patterns. This paper presents a classification scheme that uses shearlet transform based textural features for the classification of thyroid nodules in ultrasound images. The study comprised of 60 thyroid ultrasound images (30 with benign nodules and 30 with malignant nodules). Total of 22 features are extracted. Support vector machine (SVM) and K nearest neighbor (KNN) are used to differentiate benign and malignant nodules. The diagnostic sensitivity, specificity, F1_score and accuracy of both the classifiers are calculated. A comparative study has been carried out with respect to their performances. The sensitivity of SVM with radial basis function (RBF) kernel is 100% as compared to that of KNN with 96.33%. The proposed features can increase the accuracy of the classifier and decrease the rate of misdiagnosis in thyroid nodule classification.

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