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Table 10 Proposed features fusion and feature selection results on ISIC-UDA dataset

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

Method Measures   
  Sensitivity Precision Specificity FNR FPR Accuracy
DT 87.25 90.65 97.1 10.7 0.12 89.3
QDA 79.75 88.60 99.3 16.3 0.19 83.7
QSVM 98.05 98.40 99.3 1.7 0.02 98.3
LR 94.8 96.35 99.3 4.3 0.04 95.7
N-B 88.5 91.00 96.4 9.9 0.10 90.1
W-KNN 83.85 91.20 100 12.9 0.16 87.1
EBT 95.2 95.85 97.9 4.3 0.4 95.7
E-S-D 89.6 89.75 92.1 9.9 0.09 90.1
L-KNN 81.7 90.25 100 14.6 0.18 85.4
Proposed 97.85 98.60 100 1.7 0.02 98.3
  1. Data in bold are significant