Table 4.

Classification performances of the 10-fold machine learning classifiers on features extracted from each of the five pre-trained deep models.

Feature Extractor	Classifier	Accuracy	Sensitivity	Specificity	Precision	F1-Score
AlexNet	NB	${\displaystyle 0.7768\pm 0.0067}$	${\displaystyle 0.6746\pm 0.0117}$	${\displaystyle 0.8366\pm 0.0089}$	${\displaystyle 0.7079\pm 0.0112}$	${\displaystyle 0.6905\pm 0.0090}$
	SVM	${\displaystyle 0.7709\pm 0.0143}$	${\displaystyle 0.6692\pm 0.0244}$	${\displaystyle 0.8305\pm 0.0147}$	${\displaystyle 0.6989\pm 0.0214}$	${\displaystyle 0.6830\pm 0.0203}$
	LDA	${\displaystyle 0.7963\pm 0.0065}$	${\displaystyle 0.6650\pm 0.0125}$	${\displaystyle 0.8732\pm 0.0076}$	${\displaystyle 0.7547\pm 0.0114}$	${\displaystyle 0.7067\pm 0.0097}$
	DT	${\displaystyle 0.7160\pm 0.0215}$	${\displaystyle 0.6008\pm 0.0445}$	${\displaystyle 0.7834\pm 0.0286}$	${\displaystyle 0.6223\pm 0.0329}$	${\displaystyle 0.6079\pm 0.0324}$
GoogleNet	NB	${\displaystyle 0.7480\pm 0.0108}$	${\displaystyle 0.5867\pm 0.0202}$	${\displaystyle 0.8424\pm 0.0124}$	${\displaystyle 0.6864\pm 0.0178}$	${\displaystyle 0.6319\pm 0.0166}$
	SVM	${\displaystyle 0.7238\pm 0.0088}$	${\displaystyle 0.4462\pm 0.0158}$	${\displaystyle 0.8863\pm 0.0107}$	${\displaystyle 0.6984\pm 0.0215}$	${\displaystyle 0.5438\pm 0.0154}$
	LDA	${\displaystyle 0.7758\pm 0.0069}$	${\displaystyle 0.6583\pm 0.0157}$	${\displaystyle 0.8446\pm 0.0064}$	${\displaystyle 0.7127\pm 0.0099}$	${\displaystyle 0.6841\pm 0.0120}$
	DT	${\displaystyle 0.7740\pm 0.0220}$	${\displaystyle 0.6925\pm 0.0408}$	${\displaystyle 0.8217\pm 0.0239}$	${\displaystyle 0.6968\pm 0.0312}$	${\displaystyle 0.6925\pm 0.0310}$
InceptionV3	NB	${\displaystyle 0.7575\pm 0.009}$	${\displaystyle 0.6137\pm 0.0194}$	${\displaystyle 0.8417\pm 0.0093}$	${\displaystyle 0.6945\pm 0.0139}$	${\displaystyle 0.6511\pm 0.0149}$
	SVM	${\displaystyle 0.7242\pm 0.0069}$	${\displaystyle 0.4163\pm 0.0179}$	${\displaystyle 0.9044\pm 0.0033}$	${\displaystyle 0.7174\pm 0.0114}$	${\displaystyle 0.5263\pm 0.0167}$
	LDA	${\displaystyle 0.7698\pm 0.0060}$	${\displaystyle 0.5854\pm 0.0134}$	${\displaystyle 0.8778\pm 0.0056}$	${\displaystyle 0.7373\pm 0.0098}$	${\displaystyle 0.6523\pm 0.0105}$
	DT	${\displaystyle 0.6909\pm 0.0210}$	${\displaystyle 0.5300\pm 0.0409}$	${\displaystyle 0.7851\pm 0.0306}$	${\displaystyle 0.5963\pm 0.0353}$	${\displaystyle 0.5572\pm 0.0306}$
ResNet50	NB	${\displaystyle 0.6878\pm 0.0103}$	${\displaystyle 0.8013\pm 0.0135}$	${\displaystyle 0.6215\pm 0.0149}$	${\displaystyle 0.5539\pm 0.0105}$	${\displaystyle 0.6547\pm 0.0098}$
	SVM	${\displaystyle 0.7240\pm 0.0065}$	${\displaystyle 0.3167\pm 0.0115}$	${\displaystyle 0.9624\pm 0.0074}$	${\displaystyle 0.8343\pm 0.0279}$	${\displaystyle 0.4584\pm 0.0138}$
	LDA	${\displaystyle 0.7748\pm 0.0098}$	${\displaystyle 0.5375\pm 0.0221}$	${\displaystyle 0.9137\pm 0.0130}$	${\displaystyle 0.7874\pm 0.0220}$	${\displaystyle 0.6373\pm 0.0173}$
	DT	${\displaystyle 0.7665\pm 0.0159}$	${\displaystyle 0.6088\pm 0.0300}$	${\displaystyle 0.8588\pm 0.0189}$	${\displaystyle 0.7190\pm 0.0287}$	${\displaystyle 0.6575\pm 0.0242}$
XceptionNet	NB	${\displaystyle 0.7240\pm 0.0094}$	${\displaystyle 0.6667\pm 0.0001}$	${\displaystyle 0.7576\pm 0.0149}$	${\displaystyle 0.6182\pm 0.0146}$	${\displaystyle 0.6411\pm 0.0079}$
	SVM	${\displaystyle 0.7597\pm 0.0094}$	${\displaystyle 0.5258\pm 0.0219}$	${\displaystyle 0.8966\pm 0.0085}$	${\displaystyle 0.7489\pm 0.0167}$	${\displaystyle 0.6170\pm 0.0182}$
	LDA	${\displaystyle 0.7972\pm 0.0058}$	${\displaystyle 0.6662\pm 0.0100}$	${\displaystyle 0.8739\pm 0.0058}$	${\displaystyle 0.7558\pm 0.0094}$	${\displaystyle 0.7081\pm 0.0086}$
	DT	${\displaystyle 0.7403\pm 0.0243}$	${\displaystyle 0.6733\pm 0.0379}$	${\displaystyle 0.7795\pm 0.0286}$	${\displaystyle 0.6446\pm 0.0344}$	${\displaystyle 0.6567\pm 0.0313}$