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Table 7 Five-fold experiment results for the best network of each architecture

From: COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

    Sensitivity Specificity Dice G-mean F2
\(\mu\) \(\sigma\) \(\mu\) \(\sigma\) \(\mu\) \(\sigma\) \(\mu\) \(\sigma\) \(\mu\) \(\sigma\)
Binary SegNet 0.947 0.048 0.945 0.015 0.703 0.055 0.945 0.019 0.829 0.029
U-NET 0.961 0.033 0.923 0.018 0.643 0.058 0.941 0.014 0.800 0.033
Multi SegNet \({\hbox {C}}_1\) 0.653 0.043 0.927 0.030 0.425 0.084 0.778 0.035 0.535 0.069
\({\hbox {C}}_2\) 0.688 0.072 0.963 0.004 0.410 0.081 0.813 0.043 0.537 0.073
\({\hbox {C}}_3\) 0.679 0.270 0.987 0.006 0.117 0.068 0.804 0.167 0.214 0.110
U-NET \({\hbox {C}}_1\) 0.685 0.130 0.910 0.045 0.402 0.144 0.786 0.083 0.527 0.135
\({\hbox {C}}_2\) 0.666 0.120 0.973 0.013 0.458 0.093 0.801 0.066 0.550 0.052
\({\hbox {C}}_3\) 0.632 0.26 0.991 0.005 0.152 0.092 0.777 0.165 0.250 0.118
  1. Results are the mean and standard deviation of sensitivity, specificity, dice, g-mean, and F2