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Table 11 Results for individual extracted set of features using ISIC-UDA dataset

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

Method Features Performance measures   
  Color HOG Harlick Sensitivity (%) Precision (%) Specificity (%) FNR (%) FPR Accuracy (%)
Decision tree    72.75 77.4 90.7 23.6 0.62 76.4
     70.15 69.4 69.3 30.0 0.30 70.0
    86.55 87.35 91.4 12.4 0.13 87.6
QDA    74.04 74.04 79.3 24.9 0.21 75.1
     77.4 88.45 100 18.0 0.22 82.0
    82.65 83.15 87.9 16.3 0.17 83.7
QSVM    73.7 77.25 89.3 23.2 0.73 76.8
     81.35 89.3 99.3 15.0 0.18 85.0
    94.45 95.8 98.6 4.7 0.05 95.3
LR    68.5 68.35 73.6 30.5 0.31 69.5
     78.5 88.9 100 17.2 0.21 82.8
    93.4 94.65 97.1 5.6 0.05 94.4
N-B    69.4 69.95 78.6 28.8 0.30 71.2
     76.7 76.7 81.4 22.3 0.22 77.7
    86.0 89.05 95.7 12.0 0.13 88.0
W-KNN    74.04 77.9 90.0 22.7 0.21 77.3
     80.8 87.15 97.1 15.9 0.17 84.1
    88.55 92.3 98.6 9.4 0.11 90.6
EBT    71.35 71.8 79.3 27.0 0.23 73.0
     80.8 83.8 92.9 17.2 0.17 82.8
    90.5 91.55 95.0 8.6 0.09 91.4
ESD    69.95 71.6 82.9 27.5 0.30 72.5
     60.2 74.5 85.0 24.9 0.27 75.1
    83.9 86.5 93.6 14.2 0.15 85.8
Cubic KNN    71.7 74.4 86.4 25.3 0.23 74.7
     80.15 87.4 97.9 16.3 0.19 83.7
    85.5 90.2 97.9 12.0 0.14 88.0
Proposed    73.65 78.5 91.4 22.7 0.22 77.3
     82.6 87.55 96.4 14.6 0.15 85.4
    95.2 95.85 97.9 4.3 0.04 95.7