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Table 7 Performance comparison of the state-of-the-art methods and our method

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

  Method 0.125 0.25 0.5 1 2 4 8 CPM
LUNA16CAD 2D CNN 0.113 0.165 0.265 0.465 0.596 0.695 0.785 0.440
LungNess 2D CNN 0.453 0.535 0.591 0.635 0.696 0.741 0.797 0.635
iitem03 2D CNN 0.394 0.491 0.570 0.660 0.732 0.795 0.851 0.642
[22] 3D CNN 0.517 0.602 0.720 0.788 0.822 0.839 0.856 0.735
LUNA16CAD 3D CNN 0.640 0.698 0.750 0.804 0.847 0.874 0.897 0.787
[9] 2D CNN 0.734 0.744 0.763 0.796 0.824 0.832 0.834 0.790
DIAG_CONVNET [23] 3D CNN 0.636 0.727 0.792 0.844 0.876 0.905 0.916 0.814
UACNN 2D CNN 0.655 0.745 0.807 0.849 0.880 0.907 0.925 0.824
CUMedVis [24] 3D CNN 0.677 0.737 0.815 0.848 0.879 0.907 0.922 0.827
D48 3D CNN 0.676 0.765 0.839 0.894 0.922 0.938 0.953 0.855
ESB-ALL 3D CNN 0.720 0.842 0.914 0.954 0.974 0.982 0.982 0.910