Table 2.

Proposed contributions of related work

Ref.	CNN Architecture	# Of Classes	# Of samples			Performance metrics (%)			Image Enh. (IE)
			COVID	Normal	Other	Acc.	Sencov	Other
[68]	VGG16	2	403	1124	–	95	90	PPV - 95	☒
[22]	DenseNet121, VGG19, InceptionV3	2	25	25	–	90	–	F1cov - 91	☒
[73]	EfficientNet	2	100	1431	–	–	96	Sp - 70	☒
[21]	CNN + GWO	3	900	900	900	97.78	98.5	PPV - 92.8	HE
[12]	VGG16	3	132	132	132	85	100	F1–85	☒
[27]	CNN, LSTM	3	1525	1525	1525	–	99.3	F1cov - 98.9	☒
[14]	Xception	3	127	500	500	–	97	F1cov - 96.9	☒
[33]	Xception (CoroNet)	3	290	310	657	90.21	89	F1–91	☒
[5]	MobileNetV2, Inception InceptionResNetV2, VGG19	3	224	504	700	92.85	98.66	Spcov − 96.46	☒
[50]	DarkNet	3	127	500	500	87.02	97.9	F1–87.37	☒
[67]	CapsNet	3	231	1050	1050	84	94.57	F1–84.21	☒
[40]	DenseNet-121, ResNet50, ResNet18, SqueezeNet	3	184	2400	2600	–	98	Spcov - 93	☒
[52]	DenseNet169, Inception ResNet, NASNetLarge	3	108	533	515	94	–	F1 = 90 Sp - 89	TVF
[1]	VGG19, ResNet, AlexNet, GoogLeNet and squeezeNet	3	105	80	11	93.1	100	Sp - 85.1	☒
[15]	J48 + 11 CNN models	2	50	50	–	100	100	F1–100	☒
[37]	Restnet18, GoogleNet and AlexNet	4	69	79	158	80.6	100	F1–82.32	☒
[38]	CovXNet	4	305	305	610	90.2	95	F1–90.4	☒
[49]	ResNet18	4	180	191	131	88.9	92.5	F1–84.4	HE+G
[71]	VGG19, ResNet50, COVID-NET	3	268	8066	5538	93.3	91	PPVcov-98.9	☒
[39]	DenseNet121(COVID-AID)	4	155	1583	4273	90.5	100	F1–92.30	☒
[53]	AlexNet, DenseNet201, InceptionResNetV2, Xception	3	683	2924	4272	–	98.93	Sp - 98.77	☒
[24]	AlexNet	4	371	2017	–	93.42	89.18	Sp - 98.9	☒
[48]	Shallow-CNN	3	321	1583	4273	–	100	PPVcov - 99.38	☒
[16]	ASSOA	4	–	1227	3445	99.23	–	–	☒
[35]	MAG-SD, InceptionV3, VGG16, ResNet50	3	462	1602	1567	95.85	95	F1–95.54	CLAHE
		4	462	1602	4265	87.12	91	F1–86.98
[8]	WideResNet, MinMax UE	4	99	1587	4273	–	–	–	☒
[19]	MSRCovXNet	3	386	8066	5551	95	94	F1–95.33	☒
[66]	EDL-COVID	3	573	8851	6053	95	94.1	–	☒

*Bold represents best model; IE – Image enhancement, HE – histogram equalization, TVF - Total variation filter and G – gamma correction, UE- uncertainty estimation