Table 5.

Comparative analysis of the proposed approach with recently proposed strategies

Authors	Technique	Network	Classes	Accuracy	Precision	Recall	AUC	Specificity
Yujin et al. [33]	Fine tuning	Res- Net18	4	0.88	0.83	0.86	–	0.96
Afshar et al. [1]	Fine tuning	Capsule		0.95	–	0.90		0.95
Proposed	Fine	NASNet Large		0.95	0.95	0.90	0.94	0.92
Wang et al. [55]	Full training	COVID-Net	3	0.92	0.91	0.88	–	–
Apostolopoulos et al. [3]	Fine tuning	VGG19		0.87	–	0.92	–	0.98
Proposed	Fine tuning	NASNet Large		0.96	0.93	0.91	0.96	0.94
Hall et al. [14]	Fine tuning	Ensemble	2	0.91	–	0.78	0.94	0.93
Apostolopoulos et al. [3]	Fine tuning	VGG19		0.98	–	0.92	–	0.98
Proposed	Fine tuning	NASNet Large		0.98	0.87	0.90	0.99	0.98

Bold numbers highlight the results obtained using proposed approach