title: Fast and efficient lung disease classification using hierarchical one-against-all support vector machine and cost-sensitive feature selection.
abstract: To improve time and accuracy in differentiating diffuse interstitial lung disease for computer-aided quantification, we introduce a hierarchical support vector machine which selects a class by training a binary classifier at each node in a hierarchy, thus allowing each classifier to use a class-specific quasi-optimal feature set. In addition, the computational cost-sensitive group-feature selection criterion combined with the sequential forward selection is applied in order to obtain a useful and computationally inexpensive quasi-optimal feature set for the purpose of accelerating the classification time. The classification time was reduced by up to 57% and the overall accuracy was significantly improved in comparison with the one-against-all and one-against-one support vector machine methods with sequential forward selection (paired t-test, p<0.001). The reduction of classification time as well as the improvement of overall accuracy demonstrates promise for the proposed classification method to be adopted in various real-time and on-line image-based clinical applications.
authors.affiliation: Department of Industrial Engineering, Engineering College, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea.
authors.forename: Youngjoo
authors.initials: Y
authors.lastname: Lee
authors.forename: Yongjun
authors.initials: Y
authors.lastname: Chang
authors.forename: Namkug
authors.initials: N
authors.lastname: Kim
authors.forename: Jonghyuck
authors.initials: J
authors.lastname: Lim
authors.forename: Joon Beom
authors.initials: JB
authors.lastname: Seo
authors.forename: Young Kyung
authors.initials: YK
authors.lastname: Lee
mesh_terms: D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging; D008171:Lung Diseases / Q000145:classification* / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed / Q000379:methods