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Table 4 Performance comparison of our nodule classification method in each experimental setup

From: Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method

  0.125 0.25 0.5 1 2 4 8 CPM
S48 0.691 0.788 0.851 0.891 0.910 0.934 0.945 0.859
S64 0.736 0.818 0.880 0.911 0.932 0.950 0.960 0.884
D48 0.676 0.765 0.839 0.894 0.922 0.938 0.953 0.855
D64 0.710 0.800 0.870 0.902 0.924 0.943 0.958 0.872
ESB-S48 0.655 0.739 0.863 0.927 0.962 0.973 0.976 0.871
ESB-S64 0.633 0.744 0.870 0.943 0.974 0.980 0.980 0.875
ESB-S 0.683 0.813 0.911 0.954 0.969 0.982 0.982 0.899
ESB-D48 0.645 0.736 0.816 0.908 0.954 0.975 0.980 0.859
ESB-D64 0.646 0.736 0.834 0.919 0.962 0.977 0.981 0.865
ESB-D 0.679 0.778 0.878 0.937 0.963 0.981 0.981 0.885
ESB-BEST 0.734 0.814 0.895 0.934 0.957 0.971 0.976 0.897
ESB-ALL 0.720 0.842 0.914 0.954 0.974 0.982 0.982 0.910