TABLE 2. A Comparison of Statistical Performance Measures of DeepCough3D With DeepCough2D, AutoML [37], AI4COVID[19], Coswara [20], and Cough Against (Versus) Covid [36] for Recognition of COVID-19 Coughs.

					DeepCough3D				DeepCough2D
	AUC (M1)	Precision (M2)	Sensitivity (M3)	Specificity (M4)	M1	M2	M3	M4	M1	M2	M3	M4
DeepCough3D	98.80 $\pm$ 0.83	96.54 $\pm$ 1.75	96.43 $\pm$ 1.85	96.20 $\pm$ 1.74	-	-	-	-	**	**	**	**
DeepCough2D	96.20 $\pm$ 1.18	89.87 $\pm$ 1.46	89.63 $\pm$ 1.57	86.55 $\pm$ 4.64	**	**	**	**	-	-	-	-
AutoML	69.04 $\pm$ 17.50	78.28 $\pm$ 7.78	48.70 $\pm$ 23.71	63.26 $\pm$ 11.74	**	**	**	**	**	*	**	0.22
AI4COVID	92.36 $\pm$ 1.96	85.93 $\pm$ 2.87	85.87 $\pm$ 2.87	81.36 $\pm$ 4.31	**	**	**	**	**	**	**	**
Coswara	87.69 $\pm$ 3.86	84.08 $\pm$ 3.57	81.99 $\pm$ 5.47	83.45 $\pm$ 3.48	**	**	**	**	0.33	0.14	0.16	0.32
Cough-vs-Covid	66.41 $\pm$ 4.23	76.04 $\pm$ 2.53	76.64 $\pm$ 2.28	67.00 $\pm$ 4.29	**	**	**	**	**	**	**	**

The p-values for t-test are statistically significant (*: $p<0.05$, **: $p<0.01$) for the average of all statistical metrics (M1-M4) for DeepCough3D in comparison to other methods. Likewise, the p-values for t-test for DeepCough2D are also reported.