Energy Spectrum Scanning of Thyroid Nodules：A Study Based on Support Vector Machine
10.3969/j.issn.1005-5185.2015.03.022
- VernacularTitle:基于支持向量机的甲状腺结节能谱研究
- Author:
Chuangbo YANG
;
Siqiang NIU
;
Yongjun JIA
;
Yong YU
;
Haifeng DUAN
;
Taiping HE
- Publication Type:Journal Article
- Keywords:
Thyroid nodule;
Tomography,X-ray computed;
Image processing,computer-assisted;
Data interpretation,statistical;
Support vector machine
- From:
Chinese Journal of Medical Imaging
2015;(3):231-234,240
- CountryChina
- Language:Chinese
-
Abstract:
PurposeSupport vector machine (SVM) is a machine learning method based on statistical learning theory of Vapnik-Chervonenkis (VC) dimension structure and risk minimization theory. We analyzed the gem spectrum CT scan data of patients with thyroid nodules and established the SVM diagnostic model. The experimental targets were then reduced and the forecast analysis was carried out based on SVM model. The diagnostic model and experimental methods were proved to provide guidance for clinical diagnosis of thyroid nodules.
