Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74410
Title: Intelligent diagnostic methods for thyroid nodules
Authors: Jiang, Y
Deng, Z
Chen, J
Wu, H
Choi, KS 
Wang, S
Keywords: Clinical Decision Support
Intelligent Diagnosis
Machine Learning
Thyroid Nodules
Ultrasound Diagnosis
Issue Date: 2017
Publisher: American Scientific Publishers
Source: Journal of medical imaging and health informatics, 2017, v. 7, no. 8, p. 1772-1779 How to cite?
Journal: Journal of medical imaging and health informatics 
Abstract: According to size, shape, echogenicity and other ultrasonographic features, physicians can determine whether thyroid nodules are malignant or not. However, the diagnosis results might vary due to differences in physicians' expertise and experience. This paper focuses on ultrasound diagnosis of benign and malignant thyroid nodules by introducing several classical machine learning methods. These machine learning methods can establish intelligent diagnosis models to determine the conditions of new cases based on the knowledge learned from the existing ultrasound data and the confirmed diagnosis results. This paper compares and analyzes the diagnosis performance between intelligent methods and physicians as well as the performance of different machine learning methods in terms of the diagnosis accuracy, sensitivity, specificity and other metrics experimentally. Except for the decision tree, the accuracies of all of the intelligent diagnostic methods are approximately 0.84, and the AUCs are greater than 0.87, which are comparable with physicians' decision accuracies. Different intelligent methods have different characteristics. For example, some learn fast, and some have better interpretability. The study shows that machine learning-based intelligent diagnosis methods can provide physicians with clinical decision support in the diagnosis of thyroid nodules. Copyright
URI: http://hdl.handle.net/10397/74410
ISSN: 2156-7018
EISSN: 2156-7026
DOI: 10.1166/jmihi.2017.2261
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Last Week
0
Last month
Citations as of Apr 6, 2019

WEB OF SCIENCETM
Citations

1
Last Week
0
Last month
Citations as of Apr 9, 2019

Page view(s)

95
Last Week
0
Last month
Citations as of Aug 21, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.