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.

SUPPORT VECTOR MACHINE BASED AUTOMATIC CLASSIFICATION OF HUMAN BRAIN USING MR IMAGE FEATURES

2006 ◽  
Vol 06 (03) ◽  
pp. 357-370 ◽  
Author(s):  
HENRY SELVARAJ ◽  
S. THAMARAI SELVI ◽  
D. SELVATHI ◽  
R. RAMKUMAR
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Volume Data ◽  
K Nearest Neighbor ◽  
Incorrect Diagnosis ◽  
Quantitative Indication

This paper proposes an intelligent classification technique to identify two categories of MRI volume data as normal and abnormal. The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to incorrect diagnosis when a large number of MRIs are analyzed. In this work, the textural features are extracted from the MR data of patients and these features are used to classify a patient as belonging to normal (healthy brain) or abnormal (tumor brain). The categorization is obtained using various classifiers such as support vector machine (SVM), radial basis function, multilayer perceptron and k-nearest neighbor. The performance of these classifiers are analyzed and a quantitative indication of how better the SVM performance is when compared with other classifiers is presented. In intelligent computer aided health care system, the proposed classification system using SVM classifier can be used to assist the physician for accurate diagnosis.

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