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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