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Table 15 Classification results for challenge ISBI 2016 & ISBI 2017 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 (%) AUC
DT 87.5 88.0 86.0 12.4 0.125 87.6 0.86
QDA 80.0 80.0 79.0 20.0 0.200 80.0 0.86
QSVM 92.5 92.5 95.0 7.4 0.075 92.6 0.95
LR 92.0 91.5 95.0 8.2 0.08 91.8 0.95
NB 92.0 92.5 97.0 8.2 0.08 91.8 0.93
W-KNN 88.5 88.5 91.0 11.6 0.115 88.4 0.88
EBT 92.0 92.0 97.0 8.3 0.08 91.7 0.95
ESDA 89.5 89.5 91.5 10.4 0.105 89.6 0.94
Proposed 93.0 93.5 97.0 6.8 0.07 93.2 0.96
  1. Data in bold are significant