Table 7.

Quantitative comparison of COVID-19 detection methods. Accuracy (Acc.), F1-score, and the area under curve (AUC) are reported for comparison. SGD is the abbreviation for stochastic gradient descent, ReLu for rectified linear unit, lr for learning rate, and AF for activation function

Method	Optimizer	AF	LR Scheduling	Images size	Pre-processing step	Dataset	Technique	Acc.	F1-Score	AUC
DL [125]	Adam	ReLu	lr = 0.0001	224 $\times$ 224	Image resizing and data augmentation with rotation	COVID-19	VGG-16, transfer learning	97.97	–	–
DL [107]	Adam	ReLu, Softmax	lr = 0.0001 and it reduces when there is no improvement for continuous three epochs	224 $\times$ 224	Image resizing, scaling, and data augmentation	COVID-19	EfficientNet-B4, transfer learning, cross-validation	96.70	97.11	96.66
DL [76]	Adam	LeakyReLU, Softmax	lr = 0.001	128 $\times$ 128	Image resizing, and data augmentation such as rotation and zoom	COVID-19, Kaggle [112]	InceptionNet-V3, XceptionNet, ResNext, transfer learning	97.0	95.0	–
DL [58]	Adam	ReLu	lr = 0.00001 with rate decay of 0.1	224 $\times$ 224	Image resizing and data augmentation	COVID-19, Kermany [83]	SE-ResNext-50, transfer learning	97.55	–	–
DL [190]	Adam	ReLu	lr = 0.0001	224 $\times$ 224	Image resizing, normalization, and data augmentation technique such as random rotation, width shift, height shift, horizontal flip	COVID-19, Kaggle[112]	VGG16, ResNet50, EfficientNetB0, synthetic image generation, cross-validation	96.8	–	–
DL [127]	Adam	–	lr = 0.0001	512 $\times$ 512	Image normalization and resizing	CheXpert, Private	DenseNet-121, PCAM, Vision Transformer	86.4	–	94.1
DL [171]	Adam	ReLu, Softmax	lr = 0.000001 for 20 epochs and then 0.0000001	224 $\times$ 224	Image resizing, normalization and data augmentation	COVID-19, RSNA, CheXpert, MC	ResNeXt-50, Inception-v3, DenseNet-161, transfer learning	98.1	–	–