Before revision:  Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
After revision:  Finally, in Phase III, a ten-fold cross-validation method is used to select significant features. This later reduces the computational complexity and speed up the data mining process. In the third stage, Random forest classifier is employed to make a decision for two different categories of lesions (Spitz nevus or Atypical Spitz Tumors). The performance of our proposed scheme is evaluated using accuracy, sensitivity, specificity, G-mean, F- measure, ROC and AUC . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .