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Table 4 Results of individual extracted set of features using PH2 dataset

From: An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

Name Features Performance measures   
Classification Method Harlick HOG Color Sensitivity (%) Precision (%) Specificity (%) FNR (%) FPR Accuracy (%)
Decision tree    67.53 67.50 70.05 31.50 0.16 68.5
     71.67 72.1 85.0 23.0 0.11 77.0
    87.93 86.93 86.9 12.5 0.06 87.5
Quadratic discriminant analysis    70.0 68.43 70.0 30.0 0.14 70.0
     74.60 75.83 88.15 20.0 0.09 80.0
    84.6 81.9 80.65 17.0 0.08 83.0
Quadratic SVM    68.33 70.27 76.25 28.5 0.14 71.5
     82.5 83.37 92.7 13.5 0.06 86.5
    93.77 93.33 94.44 6.0 0.03 94.0
Logistic regression    63.36 64.06 70.05 34.0 0.17 66.0
     86.27 85.83 91.9 11.5 0.09 88.5
    89.2 90.43 92.55 9.5 0.04 90.5
Naive bayes    62.9 62.9 66.85 35.5 0.18 64.5
     81.25 81.93 90.65 15.0 0.07 85.0
    87.93 87.63 90.65 11.0 0.06 89.0
Weighted KNN    66.67 67.5 72.5 31.0 0.16 69.0
     81.67 83.27 92.5 14.0 0.06 86.0
    90.87 90.83 92.55 8.5 0.03 91.5
Ensemble boosted tree    68.33 67.77 68.75 31.5 0.16 68.5
     80.67 82.57 91.3 15.0 0.07 85.0
    88.37 89.47 91.3 10.5 0.04 89.5
Ensemble subspace discriminant    68.76 68.4 71.9 30.0 0.15 70.0
     87.1 87.03 91.9 11.0 0.05 89.0
    92.9 94.7 96.9 5.5 0.03 94.1
Cubic KNN    65.43 66.4 71.9 32.0 0.16 68.0
     80.4 80.8 89.4 16.0 0.07 84.0
    90.3 89.83 91.7 9.5 0.04 90.5
Proposed    69.6 72.23 75.65 28.0 0.14 72.0
     86.27 87.37 94.4 10.5 0.02 89.5
    94.6 93.97 94.4 5.5 0.02 94.5