Table 10.

Performance of 3D CNN-based models on the newly-formed test cohort with volume inputs.

Model	Model Instance	Prec.	Rec.	F1	Acc.	AUC
ResNet3D	Against Radio.	0.896	0.872	0.868	87.21%	0.964
	Against NAT	0.887	0.877	0.877	87.67%	0.891
	Model Aggregated	0.905	0.886	0.883	88.58%	0.954
SqueezeNet3D	Against Radio.	0.919	0.918	0.918	91.78%	0.951
	Against NAT	0.812	0.804	0.804	80.37%	0.860
	Model Aggregated	0.900	0.900	0.899	89.95%	0.935
deCovNet	Against Radio.	0.890	0.881	0.879	88.13%	0.903
	Against NAT	0.939	0.932	0.931	93.15%	0.939
	Model Aggregated	0.936	0.927	0.926	92.69%	0.927
ShiftNet3D	Against Radio.	0.925	0.922	0.922	92.24%	0.952
	Against NAT	0.905	0.904	0.904	90.41%	0.932
	Model Aggregated	0.943	0.941	0.940	94.06%	0.952
COVID-MTL	Against Radio. (Output 1)	0.930	0.922	0.921	92.24%	0.974
	Against NAT (Output 2)	0.950	0.945	0.945	94.52%	0.929
	Output Aggregated	0.946	0.941	0.940	94.06%	0.957