At this stage,

the number of features in order to achieve better performance will be reduced. supplier OSI-420 For this purpose, the PCA, which is an unsupervised linear feature extraction method, is used. In the PCA, due to the different units in the feature set, the correlation matrix is used instead of the covariance matrix. After implementing this process and calculating eigenvalues and variances, a set of principal components is obtained which are arranged in order to their ability in distinguishing between benign and malignant lesions. In order to determine the number of features which leads to the best classification results, the k number of sorted features is determined, and their efficiency is examined during classification. Finally, the features with maximum efficiency are selected.[24,37,38] Classification is the last step in the computerized analysis of pigmented skin lesions images in which lesion is predicted as benign or malignant. According to the previous studies, SVM have performed well in the field of skin lesions classification. In addition, this algorithm has various parameters that different data models can be separated by changing them. Therefore, SVM with radial basis function kernel is used as the classifier in this study. Radial basis function kernel has two parameters of C and γ which their optimal values are determined by a grid-search

on two sequences of C = 2−5, 2−4,…, 29, 210 and γ = 2−8, 2−7,…, 23, 24. During the grid search procedure,

ten-fold stratified cross-validation ​is performed to evaluate how well a particular combination of parameters is. After the grid-search, the target database is divided into two training and test sets. 70% of the database is used for training and the remaining 30% formed the test set. In both sets, the ratio of two benign and malignant classes is the same. Then SVM classifier with optimal parameters is trained and then tested on these two sets. In order to estimate the classification error, this procedure is performed 100 times and each time by changing the members of training and test sets, and the mean and standard deviation of the following evaluation criterion are calculated: Sensitivity: Percentage of patients who have been diagnosed correctly as patients. Sensitivity = TP/(TP + FN)      (6) Where TP and FN represent the number of AV-951 patients who have been diagnosed correctly as patient and incorrectly as healthy, respectively.[39] Specificity: Percentage of healthy people who are correctly diagnosed as healthy. Specificity = TN/(TN + FP)      (7) Where TN and FP represent the number of healthy people who have been diagnosed correctly as healthy and incorrectly as patient, respectively.[39] Accuracy: Percentage of patient and healthy individuals who have been diagnosed correctly.