Use of the Bootstrap Technique with Small Traning Sets for Computer-Aided Diagnosis in Breast Ultrasound
Resource
Ultrasound in Med. & Biol. 28(7),897-902
Journal
Ultrasound in Medicine & Biology
Pages
897-902
Date Issued
2002
Date
2002
Author(s)
DOI
20060927122919570874
Abstract
The purpose of this study was to test the efficacy of using small training sets in computer-aided
diagnostic systems (CAD) and to increase the capabilities of ultrasound (US) technology in the differential
diagnosis of solid breast tumors. A total of 263 sonographic images of solid breast nodules, including 129
malignancies and 134 benign nodules, were evaluated by using a bootstrap technique with 10 original training
samples. Texture parameters of a region-of-interest (ROI) were resampled with a bootstrap technique and a
decision-tree model was used to classify the tumor as benign or malignant. The accuracy was 87.07% (229 of 263
tumors), the sensitivity was 95.35% (123 of 129), the specificity was 79.10% (106 of 134), the positive predictive
value was 81.46% (123 of 151), and the negative predictive value was 94.64% (106 of 112). This analysis method
provides a second opinion for physicians with high accuracy. The new method shows a potential to be useful in
future application of CAD, especially when a large database cannot be obtained for training or a newly developed
ultrasonic system has smaller sets of samples. (E-mail: dlchen88@ms13.hinet.net)
Subjects
Ultrasound
Bootstrap
Decision-tree model
Publisher
臺北市:國立臺灣大學資訊工程學系
Type
journal article
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