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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%