A Support Vector Machine Classifier Reduces Inter-Scanner Variation in the HRCT Classification of Regional Disease Pattern in Diffuse Lung Disease: Comparison to a Bayesian classifier

Yongjun Chang, Jonghyuck Lim, Namkug Kim, Joon Beom Seo David A. Lynch
Medical Physics, May 2013
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Lung Texture

Date Published

This study aimed to investigate the effect of using different CT scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease. The study used two scanners, GE and Siemens, and applied SVM and Bayesian classifiers to multicenter data. The results showed that the SVM classifier achieved better classification accuracy than the Bayesian classifier for each scanner. The use of an integrated dataset along with the SVM classifier had benefits in terms of classification accuracy of HRCT images acquired with more than one scanner. The method used in this article has been commercialized in AVIEW Lung Texture, Coreline Soft.


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