Skip to main content

Table 4 Comparison of accuracy of projection methods

From: ANINet: a deep neural network for skull ancestry estimation

Internet

AlexNet (%)

Vgg-16 (%)

GoogLenet (%)

Resnet-50 (%)

DenseNet-121 (%)

SqueezeNet (%)

ANINet (%)

Local projection

       

Accuracy

90.83

62.17

95.92

96.33

97.97

92.91

98.04

Precision

89.38

69.70

96.11

96.74

97.98

93.76

98.76

Recall

93.17

57.76

96.58

98.09

98.15

93.58

98.44

F1-score

91.97

63.23

96.65

97.15

98.19

93.70

98.27

Specificity

87.46

67.89

95.01

95.09

98.55

90.88

99.01

Global projection

       

Accuracy

95.04

60.78

96.08

98.03

98.09

95.91

98.21

Precision

92.26

64.94

97.31

98.06

98.15

96.89

98.49

Recall

96.27

57.68

94.41

98.38

98.43

96.47

98.51

F1-score

94.80

61.17

95.96

98.50

98.70

96.80

98.77

Specificity

92.01

62.31

93.69

97.98

98.85

93.00

99.09

Combining two projection methods

       

Accuracy

95.56

65.00

98.22

98.30

98.36

96.28

99.03

Precision

92.97

69.92

97.32

98.49

98.57

97.04

98.82

Recall

96.83

58.26

96.97

98.51

98.77

96.87

98.86

F1-score

94.93

64.21

98.03

98.74

98.85

97.84

98.93

Specificity

94.15

68.88

96.33

98.79

98.99

94.69

99.11

  1. The significance of bold means that in a certain aspect (performance, model size), this model is the best among these models