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

Tópicos

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{ cost(1906) reduc(1198) effect(832) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

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.

Resumo Limpo

improv time accuraci differenti diffus interstiti lung diseas computeraid quantif introduc hierarch support vector machin select class train binari classifi node hierarchi thus allow classifi use classspecif quasioptim featur set addit comput costsensit groupfeatur select criterion combin sequenti forward select appli order obtain use comput inexpens quasioptim featur set purpos acceler classif time classif time reduc overal accuraci signific improv comparison oneagainstal oneagainston support vector machin method sequenti forward select pair ttest p reduct classif time well improv overal accuraci demonstr promis propos classif method adopt various realtim onlin imagebas clinic applic

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