Table 3.

Performance metrics of machine learning and deep learning models for COVID-19 diagnosis.

Model		Input(s)	COVID-19 against Radiology					COVID-19 against Nucleic Acid Test
			Prec.	Rec.	F1	Acc.	AUC	Prec.	Rec.	F1	Acc.	AUC
Single Task	RF	CT Features	0.861	0.865	0.862	86.47%	0.913	0.739	0.739	0.739	73.93%	0.819
	LGBM	CT Features	0.862	0.865	0.863	86.47%	0.921	0.761	0.762	0.761	76.19%	0.803
	ResNet3D	CT Volume	0.841	0.840	0.840	83.96%	0.901	0.723	0.724	0.724	72.43%	0.763
	deCoVnet	CT Volume	0.865	0.860	0.862	85.96%	0.907	0.771	0.772	0.771	77.19%	0.821
	SqueezeNet3D	CT Volume	0.897	0.885	0.888	88.47%	0.931	0.713	0.707	0.708	70.68%	0.768
	ShiftNet3D	CT Volume	0.896	0.887	0.890	88.72%	0.939	0.762	0.762	0.762	76.19%	0.824
COVID-MTL (Multi-task)	w/o. Shift3D	CT Volume	0.891	0.877	0.881	87.72%	0.927	0.760	0.757	0.758	75.69%	0.806
	Shift3D	CT Volume	0.891	0.882	0.885	88.22%	0.937	0.796	0.794	0.791	79.45%	0.842
	Shift3D	CT Volume, CT Features	0.912	0.902	0.905	90.23%	0.939	0.791	0.792	0.792	79.20%	0.846