Table 2.

Diagnosis of COVID-19 enabled by various transfer learning algorithms

Author	Month & Year	Data	Method	Accuracy	F1- Score	Specificity	Sensitivity
Jain et.al [96]	Oct, 2020	X-Ray	InceptionV3, Xception and ResNeXt	InceptionV3 model: 95%, ResNeXt: 93% and Xception: 96%	InceptionV3 model: 95%, ResNeXt: 96% and Xception: 86%	Not Reported	InceptionV3 model: 92%, ResNeXt: 96% and Xception: 78%
Prabira Kumar Sethy et al. [97]	March, 2020	X-Ray	Resnet50 + SVM	95.38%	91.41%	93.47%	97.29%
Wang.s et al. [98]	Jan, 2021	CT Scan	Modified Inception transfer learning followed by internal and external validation	Internal validation: 89.5% and external validation: 79.3%	Internal validation: 77% and external validation: 63%	Internal validation: 88% and external validation: 83%	Internal validation: 87% and external validation: 67%
Iason Katsamenis et al. [99]	Dec, 2020	X-Ray	Transfer Learning	Model-1:92% Model-2:95% Model-3:96%	Model-1:92% Model-2:95% Model-3:97%	Not Reported	Model-1:92% Model-2:95% Model-3:97%
Varalakshmi Perumal et al. [100]	Aug, 2020	X-Ray and CT Scan	Transfer Learning and Haralick features	93%	Not Reported	Not Reported	90%
Adi Alhudhaif et al. [101]	May, 2021	X-Ray	DenseNet-201	94.96%	92.11%	Not Reported	94.59%