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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