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Table 7 Proposed features fusion and selection results on ISIC-MSK dataset

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

Method Performance measures   
  Sensitivity (%) Precision (%) Specificity (%) FNR (%) FPR Accuracy (%)
Decision tree 92.95 93.1 94.30 6.9 0.07 93.1
Quadratic discriminant analysis 95.95 95.45 91.90 4.5 0.04 95.5
Quadratic SVM 96.25 96.10 95.60 3.8 0.03 96.2
Logistic regression 95.10 95.10 95.60 4.8 0.04 95.2
Naive bayes 92.80 93.30 95.60 6.9 0.07 93.1
Weighted KNN 95.10 95.10 95.60 4.8 0.04 95.2
Ensemble boosted tree 95.10 95.10 95.60 4.80 0.04 95.2
Ensemble subspace discriminant 95.10 95.10 95.60 4.8 0.04 95.2
Cubic KNN 89.35 90.65 95.60 10.0 0.10 90.0
Proposed 96.60 97.0 98.30 2.8 0.01 97.2
  1. Data in bold are significant