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Table 4 Quantitative evaluation of the combinations of cost-sensitive and data-level methods using CNN features

From: Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

Methods

ACC (%)

SPC (%)

SEN (%)

F1_M (%)

G_M (%)

AUC (%)

ResCNN

90.22 (0.88)a

95.80 (1.23)

76.05 (3.21)

81.41 (1.74)

85.34 (1.59)

96.26 (0.73)

ResCNN + SMOTE

90.98 (1.07)

94.72 (1.34)

80.95 (3.50)

82.97 (2.05)

87.54 (1.75)

96.24 (0.84)

ResCNN + BSMOTE

90.76 (1.40)

95.48 (1.54)

78.10 (2.94)

82.12 (2.55)

86.34 (1.79)

96.27 (0.87)

ResCNN + UNDER

90.02 (1.68)

90.91 (1.71)

87.62 (3.71)

82.67 (2.82)

89.23 (2.14)

96.27 (0.80)

CS-ResCNN

92.24 (1.30)

93.19 (1.73)

89.66 (2.86)

86.00 (2.27)

91.39 (1.49)

97.11 (0.59)

CS-ResCNN + SMOTE

92.35 (1.01)

95.08 (0.93)

85.03 (4.91)

85.74 (2.21)

89.88 (2.31)

97.36 (0.70)

CS-ResCNN + BSMOTE

92.01 (0.86)

95.48 (1.04)

82.72 (3.78)

84.89 (1.83)

88.85 (1.80)

97.22 (0.65)

CS-ResCNN + UNDER

91.83 (0.85)

92.79 (1.74)

89.25 (3.80)

85.58 (1.42)

90.97 (1.41)

97.35 (0.61)

  1. ResCNN, residual convolutional neural network; CS-ResCNN, cost-sensitive residual convolutional neural network; SMOTE, synthetic minority over-sampling technique; BSMOTE, borderline-SMOTE; UNDER, under-sampling; CS-ResCNN + SMOTE, the combination of CS-ResCNN and SMOTE methods; CS-ResCNN + BSMOTE, the combination of CS-ResCNN and BSMOTE methods; CS-ResCNN + BSMOTE, the combination of CS-ResCNN and UNDER methods
  2. Italic represent the best value in all methods
  3. aMean (standard deviation)