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Table 3 Results (expressed in percentage) of individual MCs classification experiments among 30 repetitions, no feature selection method applied

From: Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images

Classifier

Accuracy

Sensitivity

Specificity

AUC

F score

MLP

71.79 ±1.05

64.65 ±1.21

77.28 ±1.34

77.16 ±0.93

71.68 ±0.01

RF

77.03 ±0.13

60.46 ±0.21

89.77 ±0.15

80.10 ±0.06

76.35 ±0.01

SVM

73.80 ±0.0

61.39 ±0.0

83.34 ±0.0

77.87 ±0.0

73.39 ±0.0

AdaBoost

75.68 ±0.0

61.58 ±0.0

86.52 ±0.0

77.89 ±0.0

75.17 ±0.0

  1. Area Under the Curve (AUC), Multi Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM)