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Performance testing of several classifiers for differentiating obstructive lung diseases based on texture analysis at high-resolution computerized tomography (HRCT)

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Authors
Lee, Youngjoo; Seo, Joon Beom; Lee, June Goo; Kim, Song Soo; Kim, Namkug; Kang, Suk-Ho
Issue Date
2009-01
Publisher
Elsevier
Citation
Computer Methods and Programs in Biomedicine 93 (2009) 206-215
Keywords
Bayesian classifierANN (artificial neural net)SVM (support vector machine)Obstructive lung diseaseTexture analysis
Abstract
Machine classifiers have been used to automate quantitative analysis and avoid intra–interreader
variability in previous studies. The selection of an appropriate classification schemeis
important for improving performance based on the characteristics of the data set. This paper
investigated the performance of several machine classifiers for differentiating obstructive
lung diseases using texture analysis on various ROI (region of interest) sizes. 265 highresolution
computerized tomography (HRCT) images were taken from 92 subjects. On each
image, two experienced radiologists selected ROIs with various sizes representing area of
severe centrilobular emphysema (PLE, n = 63), mild centrilobular emphysema (CLE, n = 65),
bronchiolitis obliterans (BO, n = 70) or normal lung (NL, n = 67). Four machine classifiers were
implemented: naïve Bayesian classifier, Bayesian classifier, ANN (artificial neural net) and
SVM (support vector machine). For a testing method, 5-fold cross-validation methods were
used and each validation was repeated 20 times. The SVM had the best performance in
overall accuracy (in ROI size of 32×32 and 64×64) (t-test, p < 0.05). There was no significant
overall accuracy difference between Bayesian and ANN (t-test, p < 0.05). The naïve
Bayesian method performed significantly worse than the other classifiers (t-test, p < 0.05).
SVM showed the best performance for classification of the obstructive lung diseases in this
study.
ISSN
0169-2607
Language
English
URI
http://hdl.handle.net/10371/7552
DOI
https://doi.org/10.1016/j.cmpb.2008.10.008
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
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