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Table 3 Quantitative evaluation of the CS-ResCNN method and various conventional methods

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

Methods ACC (%) SPC (%) SEN (%) F1_M (%) G_M (%) AUC (%)
WT 75.49 (0.70)a 99.29 (0.90) 11.70 (1.55) 20.57 (2.42) 34.02 (2.26) 82.50 (1.93)
WT-SMOTE 78.34 (1.35) 89.34 (0.97) 48.84 (2.52) 55.06 (2.81) 66.05 (2.04) 83.18 (2.05)
WT-BSMOTE 78.85 (1.48) 91.73 (0.79) 44.35 (3.48) 53.24 (3.72) 63.75 (2.79) 83.89 (2.37)
WT-UNDER 72.98 (2.07) 70.00 (2.63) 80.95 (3.37) 61.96 (2.36) 75.25 (2.02) 83.40 (2.38)
LBP 81.70 (0.68) 94.67 (0.78) 46.94 (3.15) 58.18 (2.35) 66.63 (2.06) 86.04 (1.66)
LBP-SMOTE 82.40 (1.37) 91.52 (1.37) 57.96 (2.38) 64.16 (2.58) 72.82 (1.76) 86.78 (1.81)
LBP-BSMOTE 82.18 (1.07) 92.18 (1.13) 55.37 (1.77) 62.81 (2.03) 71.44 (1.36) 86.57 (1.80)
LBP-UNDER 79.22 (2.16) 80.81 (1.82) 74.97 (4.45) 66.22 (3.50) 77.81 (2.80) 86.33 (1.92)
SIFT 83.81 (0.91) 96.24 (1.05) 50.48 (2.12) 62.88 (2.01) 69.69 (1.47) 91.14 (1.44)
SIFT-SMOTE 85.43 (2.20) 92.59 (1.24) 66.26 (4.94) 71.16 (4.56) 78.30 (3.37) 91.34 (1.32)
SIFT-BSMOTE 84.88 (1.21) 92.74 (1.44) 63.81 (2.86) 69.63 (2.32) 76.91 (1.74) 91.31 (1.34)
SIFT-UNDER 81.29 (1.57) 79.54 (1.85) 85.99 (2.07) 71.43 (2.02) 82.69 (1.52) 91.29 (1.11)
COTE 78.15 (1.10) 92.34 (1.26) 40.14 (3.97) 49.89 (3.47) 60.81 (2.91) 82.19 (2.34)
COTE-SMOTE 76.60 (1.07) 80.46 (0.74) 66.26 (3.35) 60.59 (2.26) 72.99 (1.92) 81.41 (1.68)
COTE-BSMOTE 76.75 (1.32) 80.71 (0.65) 66.12 (3.83) 60.68 (2.72) 73.03 (2.28) 80.98 (1.61)
COTE-UNDER 72.09 (1.52) 69.85 (1.65) 78.10 (3.74) 60.32 (2.24) 73.83 (1.93) 80.87 (1.73)
ResCNN 90.22 (0.88) 95.80 (1.23) 76.05 (3.21) 81.41 (1.74) 85.34 (1.59) 96.26 (0.73)
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)
  1. The random forest classifier is employed for the conventional methods. ResCNN, residual convolutional neural network; CS-ResCNN, cost-sensitive residual convolutional neural network; WT, wavelet transformation; LBP, local binary pattern; SIFT, scale-invariant feature transform; COTE, color and texture features; SMOTE, synthetic minority over-sampling technique; BSMOTE, borderline-SMOTE; UNDER, under-sampling; ACC, accuracy; SPC, specificity; SEN, sensitivity; F1_M, F1-measure; G_M, G-mean; AUC, area under the receiver operating characteristic curve
  2. Italics represents the best value in all methods
  3. aMean (standard deviation)