Table 4.

Summary report of work done on experimental setup in the studied papers

Author	Model	Layer	Kernel Size	Pool size	Stride, Batch Size	Image Size
Panwar et al. [57]	VGG-19	Conv:16, Maxpool:5, FCNN:3,	3 × 3	2 × 2	Batch size:16, Stride:2	512 × 512
Nath et al. [34]	CNN	Conv:6, BN:6, Pool:4	3 × 3	2 × 2	Stride:2	256 × 256
Kassani et al. [97]	MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionRes-NetV2, VGGNet, NASNet	Standard layer				331 × 331NASNetLarge, 224 × 224NASNetMobile, 600 × 450
Hussain et al. [93]	CoroDet	Conv:9, pool:9, dense:2, ft:1,LR:1			Batch size:10	256 × 256
Gilanie et al. [135]	CNN	Conv:8, pool:2, fc:4	3 × 3	2 × 2	Batch size:128	512 × 512
Silva et al. [106]	EfficientNet	Conv:3, Mbconv:7	3 × 3		Batch size:32	104 × 153, 484 × 416
Turkoglu[49]	MKs-ELM-DNN	DenseNet201:3 STD layer block + ELM			Batch size:100	224 × 224
Horry et al. [101]	VGG16/19, Resnet50, Inception V3, Xception, InceptionResNet, DenseNet, NASNetLarge	Standard layer			Batch size:2 and 16	224 × 224( VGG), 299 × 299 (Inception)
Dutta et al. [112]	CNN, Inception V3				Batch size:32
Mertyüz et al. [51]	VGG-16, ResNet, GoogleNet,	Standard layer	3 × 3	1 × 1	Batch size:8
Ko et al. [38]	FCONet, VGG16, ResNet-50, Inception-v3, Xception	Standard layer, fc:2			Batch size:32	256 × 256
Alazab et al. [67]	VGG16	Standard layer	3 × 3	1 × 1	Batch size:25	224 × 224
Sharma et at [68]	VGG, MobileNet, Xception, DenseNet, InceptionResNet	Standard layer			Batch size:8	224 × 224
Apostolopoulos et al. [66]	VGG19, MobileNetv2, Inception, Xception, Inception-ResNetv2	Standard layer			Batch size:64	200 × 266
Wu et al. [115]	Covid-AL		3 × 3	1 × 1	Batch size:30	352 × 320
Al-Waisy et al. [39]	COVID-CheXNet (HRNet, ResNet34)		7 × 7	3 × 3	Batch size:100
Haghanifar et al. [65]	COVID-CXNet					224 × 224
Sarker et al. [69]	COVID-DenseNet	DenseB:4, TraLayer:3			Batch size:5	224 × 224
Wang et al. [70]	COVID-Net		7 × 7 to 1 × 1		batch size:64
Mangal et al. [63]	CovidAID(ChexNet, Covid-Net)				Batch size:16	224 × 224
Javaheri et al. [40]	CovidCTNet(BCDU-Net, U Net)	3DConv:10 3DMaxPool:5, Dense:2				128 × 128
Elkorany et al. [41]	COVIDetection-Net (ShuffleNet, SqueezeNet)					300 × 300
Tabik et al. [46]	COVIDSDNet				Batch size:16
Ucar et al. [103]	COVIDiagnosis-Net (Bayes-SqueezeNet)		3 × 3	1 × 1	Batch size:32	227 × 227
Hemdan et al. [42]	COVIDX-Net (VGG19, DenseNet201, InceptionV3,ResNetV2, InceptionResNetV2, Xception, MobileNetV2)	Standard layer			Batch size:7	224 × 224
Kedia et al. [48]	CoVNet-19 (DenseNet121, VGG16)	Dense layer: 32			Batch size:32	224 × 224
Ouchicha et al. [45]	CVDNet	Conv:9, max pool:9, concat:1, ftn:1,fc:3	5 × 5,	2 × 2	Batch size:8	256 × 256
Javor et al. [91]	ResNet50	Standard Layer			Batch size:32	448 × 448
Ismael et al. [98]	ResNet18, ResNet50, ResNet101, VGG16, VGG19	Conv:5, ReLU:5, BN:5	3 × 3	1 × 1		224 × 224
Rohila et al. [124]	ReCOV-101 (ResNet50, ResNet101,DenseNet169, DenseNet201)	Conv2D:23, pool:1	3 × 3	2 × 2		224 × 224
Padma et al. [35]	2DCNN			2 × 2
Jain et al. [64]	Inception V3, Xception, ResNeXt	Standard Layer				128 × 128
Anwar et al. [107]	EfficientNet B4	Standard Layer			Batch size:16	348 × 348
Sethi et al. [104]	Inception V3, ResNet50, MobileNet, Xception	Standard Layer			Batch size:32
Ying et al. [43]	DRE-Net (ResNet50)				Batch size:15	512 × 512
Jiang et al. [36]	VGG, ResNet, Inception-v3, DenseNet, InceptionResNetv2,	Standard Layer			Batch size:4	512 × 512
Yang et al. [92]	DenseNet	DenseBlock:4, pool:1, linear:1			Batch size:32
Serener et al. [111]	ResNet50, ResNet18, MobileNetV2, VGG, SqueezeNet, AlexNet, DenseNet121					224 × 224
Basu et al. [28]	DETL(AlexNet, VGGNet, ResNet50)	Alex:8, Vgg:16, Res:50	11 × 11	3 × 3
Wang et al. [95]	RestNet50, ResNet101, ResNet152				Batch size:64
Arellano, Ramos [53]	DenseNet121	Standard Layer
Voulodimos et al. [96]	FCN-8, U-Net		3 × 3	1 × 1		630 × 630
Chen et al. [1]	ResNet50, Unet +  +					512 × 512
Wu et al. [50]	ResNet50	Standard Layer			Batch size:4	256 × 256
Minaee et al. [56]	Deep-COVID (ResNet 18, ResNet50, Squeeze Net, DenseNet-121)		3 × 3	1 × 1	Batch size:20	224 × 224
Demir[99]	DeepCoroNet	11 layers			Batch size:6	100 × 100
Perumal et al. [23]	Resnet50, VGG16, InceptionV3	Standard layer	3 × 3	2 × 2	Stride:1, Batch size:250	226 × 226
Sheykhivand et al. [52]	Inception V4	Conv:4,pool:4, lstm:2: fc:2	4 × 4	1 × 1	Batch size:10	224 × 224
Mishra et al. [55]	CovAI-Net (Inception, DenseNet, Xception)	Conv:8, pool:4, d:2,	7 × 7	3 × 3	Stride:2, Batch size:32	224 × 224
Shah et al. [108]	CTNet-10 (DenseNet-169, VGG-16, ResNet-50, InceptionV3, VGG-19)	Conv:5, pool:3, fc:3			Batch size:32	128 × 128 CTNet10, 224 × 224 VGG19
Sakib et al. [100]	DL-CRC				Batch size:8
Tang et al. [44]	EDL-COVID	6 layers of CovidNet	3 × 3	1 × 1	Batch size:64
Saha et al. [94]	EMCNet(AlexNet, VGG 16, Inception, ResNet-50)	Conv:6, pool:5, ft:1, db:6, fc:2,	7 × 7	3 × 3	Batch size:32	224 × 224
Khan et al. [102]	H3DNN(3D ResNets, C3D, 3DDenseNets, I3D, LRCN)	Conv:9, pool: 6, incep:9,fc:2	7 × 7	3 × 3	Batch size:2	224 × 224
Gupta et al. [54]	InstaCovNet-19 ( NasNet, (InceptionV3, Xception, ResNet101, MobileNetV2)		3 × 3	1 × 1	Batch size:16	224 × 224

Author	Learning Rate	Epoch	Performance	Activation function	Optimizer	Software
Panwar et al. [57]		47	Accuracy:95.61%, Sensitivity:94.04%, Specificity:95.86%	Softmax	Adam
Nath et al. [34]	0.001	15	Accuracy X-ray:99.68%, CT:71.81%	ReLU	SGDM, Adam, RmsProp	Matlab 2019b
Kassani et al. [97]			Accuracy:99%			Keras package with Tensor flow
Hussain et al. [93]	0.0001	50	Accuracy: 99.1%, Precision:99.27%, Recall:98.17%, F1-score:98.51%	Sigmoid, ReLU, Leaky ReLU	Adam	Google Colab Keras, Tensorflow 2.0
Gilanie et al. [135]	0.01	25	Accuracy:96.68%, Specificity:95.65%, Sensitivity:96.24%	ReLU, Softmax		MATLAB 2018b
Silva et al. [106]	0.001	20	Accuracy:87.6%, F1-score:86.19%, AUC:90.5%	ReLu, Sigmoid	Adam	TensorFlow Keras
Turkoglu[49]		100		ReLUELM, PReLU-ELM, TanhRL
Horry et al. [101]	0.001, 0.00001	10	Precision: 86% for X-ray, 100% for LUS, 84% for CT scans			Keras APIs with a TensorFlow 2
Dutta et al. [112]	0.00003	31	Accuracy:84%	Sigmoid	RMSprop	Google Colab TensorFlow
Mertyüz et al. [51]	0.00001	20	Accuracy:96.90%, Sensitivity:95.45%, Specificity:100%	ReLu, Sigmoid		Anaconda Spyder, Keras
Ko et al. [38]	0.0001	optimal epoch	Sensitivity:99.58%, Specificity:100.00%, Accuracy:99.87%	SoftMax	Adam	TensorFlow package, Keras
Alazab et al. [67]	0.1	25	F-Measure:99%			Pandas, Scikit, TensorFlow
Sharma et at [68]	0.0001	100	Accuracy:98%, Sensitivity:100%, Specificity:100%, Precision:100%,	Relu	Adam	sklearn, tensorflow, keras
Apostolopoulos et al. [66]		10	Accuracy:96.78%, Sensitivity:98.66%, Specificity:96.46%	Relu	Adam
Wu et al. [115]	0.001	30	Accuracy:86.60%, Precision:96.20%, AUC:96.80%	Softmax
Al-Waisy et al. [39]	0.01	10, 20	Accuracy:99.99%, Sensitivity:99.98%, Specificity:100%, Precision:100%, F1-score:99.99%, MSE:0.011%, RMSE:0.012%		Adam, SGD
Haghanifar et al. [65]	0.0001	100	Accuracy:98.68%, F-score:94%	Sigmoid	Adam
Sarker et al. [69]	0.00001		Accuracy:94%, Precision:94%, Recall:94%, F-score:94%	Softmax	Adam
Wang et al. [70]	0.0002	22	Accuracy:93.3%, Sensitivity:100%, Precision:80%		Adam	Keras, Tensor- Flow
Mangal et al. [63]	0.0001	30	Accuracy:99.94%, Sensitivity100%, Precision:93.80%	Sigmoid	Adam
Javaheri et al. [40]	0.001		Accuracy:91.66%, Sensitivity:87.5%, Specificity:94%, AUC:95%		Adam
Elkorany et al. [41]			Recall:94.45%, Specificity:98.15%, Precision:94.42%, F1-score:94.4%	Softmax
Tabik et al. [46]	0.0002	100	Accuracy:76.18%, Sensitivity:72.59%, Specificity:78.67%, F1-score:75.71%,	Softmax	SGD, Adam
Ucar et al. [103]	0.0004	35	Accuracy:98.26%, Specificity:99.13%, F1-score:98.25%	ReLU		MATLAB
Hemdan et al. [42]	0.001	50	Accuracy:90%, F1-score:91%		SGD	Keras package with TensorFlow2
Kedia et al. [48]	0.001	5 to 10	Accuracy:99.71%,	ReLU, Softmax	Adam	Keras,TensorFlow2 Scikit-Learn
Ouchicha et al. [45]	Adaptive LR	20	Accuracy:96.69%, Precision:96.72% Recall:96.84%, F1-score:96.68%	Softmax	Adam
Javor et al. [91]		17	Accuracy:95.6%, Sensitivity:99.3%, Specificity:75.8%
Ismael et al. [98]	0.01	10	Accuracy:94.7%, Sensitivity:91.00%, Specificity:98.89%, F1-score94.79%, AUC:99.90%	ReLU	SGDM	MATLAB
Rohila et al. [124]	0.0001	100	Accuracy:94.9%	ReLU	Adam, SGD
Padma et al. [35]			Accuracy: 99.2%, Sensitivity:99.1%, Specificity:98.8%, Precision:100%	ReLU
Jain et al. [64]	0.001	14	Accuracy:97.97%	LeakyReLU	Adam
Anwar et al. [107]	0.0001	25	Accuracy:89.7, F1-score:89.6%, AUC:89.5%		Adam	Google colab
Sethi et al. [104]		200	Accuracy:98.6%, Specificity:99.3%, Precision:87.8%, Sensitivity:87.8%, F1-score:87.8%		Adadelta	Google Colab
Ying et al. [43]			Accuracy:98.6%, Specificity:99.3%, Precision:87.8%, Sensitivity:87.8%
Jiang et al. [36]	0.0002		Accuracy:98.92%, Recall:97.80%, Precision:100.00%, F1-score:98.89%		Adam	TensorFlow
Yang et al. [92]	0.9	20	Accuracy:95%, Sensitivity:100%, Specificity:90%, F1-score:95%, AUC:99%		SGD	Sklearn 0.22.1
Serener et al. [111]			Accuracy:89%, Sensitivity:98%, Specificity:86%, AUC:95%		SGD
Basu et al. [28]	0.0001	100	Accurary:99%	ReLU	Adam, SGD	TensorFlow, Keras
Wang et al. [95]	0.0001	50	Accuracy:96.1%	Softmax
Arellano, Ramos [53]			Sensitivity:89.47%, Specificity:100%	ReLU	Gradient Descent	Pandas, seaborn, matplotlib, Keras,
Voulodimos et al. [96]			Accuracy:99%, Recall:89%, Precision:91%, F1-Score:89%			Keras, TensorFlow Google Colab
Chen et al. [1]			Accuracy:96%, Sensitivity:98%, Specificity:94%, PPV:94.23%, NPV:97.92%			Keras
Wu et al. [50]	0.00001		Accuracy:76%, AUC:81.9%, Sensitivity:81.1%, Specificity:61.5%		rmsprop	Keras 2.1.6
Minaee et al. [56]	0.0001	100	Sensitivity:98%, Specificity:92.9%	SoftMax	Adam
Demir[99]	0.001	150	Accuracy:100%, Sensitivity:100%, Specificity:100%	SoftMax	SGDM	MATLAB 2020a
Perumal et al. [23]			Accuracy:93.8%	ReLu	Adam
Sheykhivand et al. [52]	0.001	200	Accuracy:99.5%, Sensitivity100%, Specificity:99.02%	Leaky-Relu	RMSProp	Keras, Tensorflow
Mishra et al. [55]	0.001	60	Accuracry:98.31%, Precision:100%, Sensitivity:96.74%, Specificity:100%, F1-score:98.34%, PPV:100%	ReLU, Softmax	Adam	Tensorflow, Keras
Shah et al. [108]	0.0001	30	CTNet Accuracy:82.1%, VGG19 Accuracy: 94.52%	Sigmoid	RMSProp, Adam	Google Colab
Sakib et al. [100]	0.001	100	Accuracy:93.94%, AUC:95.25%	Leaky-Relu	Adagrad	Keras, TensorFlow
Tang et al. [44]	0:002	50	Accuracy:95%, Sensitivity:96.0%, PPV:94.1%			TensorFlow 2.0.0
Saha et al. [94]	0.0001	50	Accuracy:98.91%, Precision:100%, Recall:97.82%, F1-score:98.89%	ReLu
Khan et al. [102]	0.00001	1000	Accuracy:85%	Softmax	Adam	Keras TensorFlow
Gupta et al. [54]	Reduced L.R		Accuracy:99.53%, Precision:100%, Recall:99%, F1-score:99%	SoftMax, Relu	Adam	NumPy, Scikit-Learn, TensorFlow 2