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Table 6 Evaluation indexes of different models

From: Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism

Differentiation type

Metrics

VGG16

ResNet50

ResNet50_CBAM

SENet

SKNet

Poorly differentiated

Precision

70.43

90.08

90.48

92.02

92.22

Recall

77.98

96.43

95.24

99.41

96.43

F1 Score

74.01

93.15

92.80

95.11

94.28

Moderate differentiation

Precision

77.56

96.60

98.42

98.21

96.21

Recall

79.12

90.99

94.28

92.59

93.94

F1 Score

78.33

93.71

96.30

95.32

95.06

Well differentiated

Precision

85.07

96.15

95.07

95.50

97.17

Recall

73.08

95.94

94.76

95.30

95.30

F1 Score

78.62

96.04

94.92

95.40

96.22

  1. Bold indicates the highest accuracy
  2. Unit:%