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

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

ISSN: 1471-2342