Table 5.

Comparison of the proposed method with other DL-based methods.

Study	Type of Images	Number of Samples	Method Used	Accuracy (%)
Apostolopoulos et al.28	Chest X-ray	1428	VGG-19	93.48
Wang et al.29	Chest X-ray	13,645	COVID-Net	92.40
Sethy et al.30	Chest X-ray	50	ResNet 50 + SVM	95.38
Hemdan et al.31	Chest X-ray	50	COVIDX-Net	90.00
Narin et al.33	Chest X-ray	100	Deep CNN ResNet-50	98.00
Song et al.35	Chest CT	1485	DRE-Net	86.00
Wang et al.48	Chest CT	453	M-Inception	82.90
Zheng et al.49	Chest CT	542	UNet + 3D Deep Network	90.80
Xu et al.50	Chest CT	443	ResNet + Location Attention	86.60
Ozturk et al.12	Chest X-ray	625	DarkCovidNet	98.08
		1125		89.33
Proposed Method	Chest X-ray	625	DenseNet169 + XGBoost	98.24
		1125		89.70