Table 5.

The performance comparison literature about COVID-19 diagnostic methods using chest X-ray images

Previous study	Data type	Methods/classifier	Number of classes	Accuracy (%)
Narayan Das et al. [9]	X-ray	Xception	3	97.40
Singh et. al. [52]	X-ray	MADE-based CNN	2	$94.65\mp 2.1$
Afshar et al. [30]	X-ray	Capsule Networks	4	95.7
Ucar and Korkmaz [24]	X-ray	Bayes-SqueezeNet	3	98.26
Khan et al. [21]	X-ray	CoroNet	4	89.60
Sahinbas and Catak [26]	X-ray	VGG16, VGG19, ResNet	2	80
		DenseNet and InceptionV3
Medhi et al. [27]	X-ray	Deep CNN	2	93
Zhang et al. [16]	X-ray	CAAD	2	95.18
Apostopolus et al. [25]	X-ray	VGG-19	3	93.48
Narin et al. [31]	X-ray	InceptionV3, ResNet50, Inception-ResNetV2	2	98
This study	X-ray	InceptionV3, ResNet50, ResNet101	2 (COVID-19/Normal)	96.1
		ResNet152, Inception-ResNetV2
This study	X-ray	InceptionV3, ResNet50, ResNet101	2 (COVID-19/Viral Pne.)	99.5
		ResNet152, Inception-ResNetV2
This study	X-ray	InceptionV3, ResNet50, ResNet101	2 (COVID-19/Bacterial Pne.)	99.7
		ResNet152, Inception-ResNetV2