Table 4.

Comparison of different classification studies

Study	Dataset	Method	Classifier	Accuracy %
Barstugan et al. [22]	Abdominal CT images: 16 × 16	− Grey-level co-occurrence matrix (GLCM) − Local directional pattern (LDP) − Grey-level run length matrix (GLRLM) − Grey-level size zone matrix (GLSZM) − Discrete wavelet transform (DWT)	Support vector machine (SVM)	99.68
	32 × 32			99.37
	48 × 48			99.64
	64 × 64			97.28
Basu et al. [23]	Chest X-ray images	Domain extension transfer learning (DETL)	Machine learning (ML) Deep learning (DL)	95.3% ± 0.02
Ozturk et al. [24]	Raw chest X-ray images:	DarkCovidNet model	Deep learning (DL)	98.08
	− Binary classification (COVID versus no-findings)
	− Multi-class classification (COVID versus no-findings versus pneumonia)			87.02
Elasnaoui et al. [25]	X-ray and CT images	Models: Inception_ResNet_V2	Multilayer perceptron classifier	92.18
	DenseNet201			88.09
	Resnet50			87.54
	MobileNet_V2			85.47
	Inception_V3			88.03
	VGG16			74.84
	VGG19			72.52
Proposed study	DNA sequences:	− Discrete Fourier transform	K-nearest neighbor algorithm	100
		− Discrete cosine transform
	− COVID-19 versus SARS-CoV	− Seven moment invariants
	− COVID-19 versus MERS-CoV			100
	− COVID-19 versus SARS-CoV versus MERS-CoV			100
	− COVID-19 versus SARS-CoV		Trainable cascade-forward back propagation neural network	100
	− COVID-19 versus MERS-CoV			98.34
	− COVID-19 versus SARS-CoV versus MERS-CoV			98.89