A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images

Table 1

Summary of related work.

Works	Number of classes	Models used	Accuracy	Precision	Sensitivity	Specificity
[35]	COVID-19	ResNet50	93.01%	95.18%	91.45%	94.77%
	Non-COVID-19
[36]	COVID-19	CovXNet	90.2%	90.8%	89.9%	89.1%
	Normal
	Viral pneumonia
	Bact pneumonia
	COVID-19		89.6%	88.5%	90.3%	87.6%
	Viral pneumonia
	Bact pneumonia
[37]	COVID-19	CoroNet	89.6%	90%	89.92%	96.4%
	Normal
	Viral pneumonia
	Bact pneumonia
	COVID-19		95%	95%	96.9%	97.5%
	Normal
	Bact pneumonia
[38]	COVID-19	AlexNet	78.92%	N/A	89.21%	68.63%
		VGG16	83.33%	N/A	80.39%	86.27%
		VGG19	85.29%	N/A	92.16%	78.43%
		SqueezeNet	82.84%	N/A	78.43%	87.52%
		GoogLeNet	85.29%	N/A	81.37%	90.20%
	Viral pneumonia	MobileNetV2	92.16%	N/A	97.06%	87.25%
		ResNet18	91.61	N/A	95.10%	88.23%
		ResNet50	94.12%	N/A	90.20%	100%
		ResNet101	99.51%	N/A	100%	99.02%
		Xception	99.02%	N/A	98.04%	100%
[39]	Normal	VGG16	79.01%	N/A	N/A	N/A
		DenseNet121	89.96%	N/A	N/A	N/A
	COVID-19	Xception	88.03%	N/A	N/A	N/A
		NASNet	85.03%	N/A	N/A	N/A
	Other	EfficientNet	93.48%	N/A	N/A	N/A
[40]	COVID-19	AlexNet	99.13%	N/A	99.47%	99.15%
	Healthy
	Pneumonia
	Tuberculosis
[41]	COVID-19	Majority voting method	99.31%	100%	100%	N/A
	Normal
	Viral pneumonia
[42]	COVID-19	DenseNet	97.99%	98.38%	98.15%	N/A
	Normal
	Pneumonia
[43]	COVID-19	Majority voting method	99.26%	97.87%	100%	98.89%
	Normal
	Pneumonia
[44]	COVID-19	ResNet50V2	95.49%	96.85%	99.19%	98.27%
	Normal	VGG16	92.70%	97.50%	94.35%	98.69%
	Pneumonia	Inception V3	92.97%	97.60%	98.39%	98.67%
[45]	COVID-19	VGG16	91.69%	92.33%	95.92%	100%
	Normal
	Pneumonia
[46]	COVID-19	VGG16	87.84%	82.00%	82.33%	91.20%
	Normal	Inception V3	91.32%	87.54%	89.00%	94.00%
	Viral pneumonia	EfficientNetB0	92.93%	88.30%	90.00%	95.00%