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Table 1 Performance comparison of CADx systems by sensitivity

From: Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine

Author Classifier Sensitivity
Jing Z. et al. (2010) [12]. Ruled-based support vector machine 84.39%
Lee M. et al. (2010) [17]. Genetic algorithm with the random subspace method 95%
Anand S. K. V. (2010) [18]. Artificial neural network /inference and forecasting 89.6%
Kumar S A. et al. (2011) [13]. Fuzzy system 90%
Dmitriy Z. et al. (2011) [19]. Decision trees 69%
Chen H. et al. (2012) [14]. Artificial neural network and multivariable logistic regression 90%
Kumar S. A. et al. (2013) [15]. Artificial neural network 89.1%
Keshani M. et al. (2013) [16]. Support vector machine 89%
Zhang F. et al. (2014) [20]. Support vector machine and probabilistic latent semantic analysis 83%
Kuruvilla J. et al. (2014) [21]. Neural network 91.4%
Our method (2015) Support vector machine with radial basis function 90.90%