Fast and efficient lung disease classification using hierarchical one-against-all support vector machine and cost-sensitive feature selection

Authors
Yongjun Chang, Namkug Kim, Youngjoo Lee, Jonghyuck Lim, Joon Beom Seo, Young Kyung Lee
Journal
Computers in Biology and Medicine, 2012
Related Product

Lung Texture

Date Published
2012
Summary

A hierarchical support vector machine (SVM) with class-specific feature selection was proposed to enhance the accuracy and reduce the classification time for differentiating diffuse interstitial lung disease in computer-aided quantification. This method allowed each binary classifier to use a class-specific quasi-optimal feature set, and a computational cost-sensitive group-feature selection criterion was applied for accelerating the classification time. Compared to one-against-all and one-against-one SVM methods with sequential forward selection, the proposed method reduced classification time by up to 57% and significantly improved overall accuracy (paired t-test, p<0.001). These results suggest potential for the proposed method in real-time and online image-based clinical applications. This study also had an impact on the development of AVIEW Lung Texture software.

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