[40] The described classification procedure is implemented on the database with all the extracted features and Hedgehog Pathway also, on the k number of features obtained

by the reducing method. Then the optimal feature set which has the lowest number of members and also, the highest AUC value, is selected. RESULTS The results of the proposed methods in preprocessing stage have been examined by dermatologist. According to the medical doctor diagnosis, these methods detect the boundaries of lesions with high accuracy and determine the lesion area with accuracy of extent of 100% for the used database in this study. In the classification stage, initial experiment was conducted on the database with all the extracted features. The optimal parameters which are found by the grid search and 10-fold cross-validation for this input set, have values ​of (C*, γ*) = (32, 0.0078). Applying 100 times of SVM classifier with these optimal parameters on the training and test sets of 197 and 85 members respectively, leads to 81.13% ± 3.25% accuracy, 75.66% ± 6.87% sensitivity, 86.14% ± 5.27% specificity and 0.87 ± 0.03 area under the characteristic curve. Then, with the goal of reducing the computation time and increasing the efficiency of the classifier, the classification

procedure runs on the k features obtained by dimension reduction method. Due to the complexity of the problem in this study, it does not seem that small number of features has ability to make distinctions between classes very well. On the other hand, the large number of features may result in poor performance of the classifier. With these assumptions, k ranges between 5 and 60. Figure 8 shows mean values of the AUC of 100 times classification with the optimal parameters versus the subset size of the principal components which are obtained by PCA. In this figure, it is observed that highest value of the

AUC corresponds to the subset of principle components with size of 13, which has the mean value of 0.881. The mean and standard deviation of accuracy, sensitivity and specificity for this subset are 82.2% ± 3.57%, 77.02% ± 5.97% and 86.93% ± 5.46%, respectively which are obtained for optimal parameters (C*, γ*) = (256, 0.0078). In Cilengitide Figure 9 which shows the mean values of accuracy, sensitivity and specificity versus the size of principal components subsets, ​the mentioned values can be observed. Figure 8 The mean values of area under the curve versus the size of principal components subset Figure 9 The mean values of accuracy, sensitivity and specificity versus the size of principal components subset Table 1 shows results of classification for the optimal number of features selected by the feature selection method and also, for all the extracted features.