TABLE 4.

Experimental results of the feature set created by concatenating features from two architectures (COVID‐19 image Dataset)

Feature extractor	Classifier	Acc. (%)	SN (%)	SP (%)	PREC (%)	Time(s) (%)
AlexNet + EfficientNet‐b0	LDA	98.0	99.0	100	100	4.651
	SVM	97.2	98.0	99.2	99.0	4.589
	KNN	94.0	91.8	100	100	3.864
	DT	81.3	88.2	90.0	87.5	4.958
	NB	94.0	98.1	97.1	96.4	10.757
AlexNet + NASNetLarge	LDA	94.0	94.5	98.5	98.1	4.003
	SVM	96.0	98.1	98.5	98.1	3.791
	KNN	90.4	94.5	97.1	96.3	3.542
	DT	80.5	90.0	90.7	88.4	3.661
	NB	93.2	96.3	96.4	95.5	10.538
AlexNet + Xception	LDA	92.8	94.5	100	100	4.053
	SVM	96.8	98.1	100	100	3.997
	KNN	92.0	92.7	100	100	3.883
	DT	83.9	90.9	95.7	94.3	3.854
	NB	95.6	97.2	100	100	11.171
EfficientNet‐b0 + NASNetLarge	LDA	97.2	98.1	99.2	99.0	4.052
	SVM	97.2	97.2	99.2	99.0	4.321
	KNN	91.6	92.7	98.5	98.0	4.038
	DT	76.5	84.6	87.8	84.6	3.956
	NB	93.6	97.2	97.8	97.2	11.139
EfficientNet‐b0 + Xception	LDA	96.0	97.2	100	100	4.217
	SVM	97.2	97.2	100	100	4.153
	KNN	96.4	95.4	100	100	4.019
	DT	80.1	87.3	93.3	90.6	4.010
	NB	93.6	95.4	100	100	11.475
NASNetLarge + Xception	LDA	94.8	95.4	97.8	97.2	4.760
	SVM	97.6	99.0	99.2	99.0	4.485
	KNN	90.8	91.8	97.1	96.2	3.862
	DT	80.1	90.9	90.0	87.8	3.942
	NB	94.8	97.2	98.5	97.2	12.314