TABLE III. Performance Comparison of Four Architectures and Eight Classifiers (32 Combinations) for X-Ray Images. All Values are Given in Percent.

Model	Classifier	Accuracy	Sensitivity	Specificity	AUC
DenseNet121	AdaBoost	91 ± 5.9	77.7 ± 15.8	95.4 ± 5.2	96.6 ± 3.9
	Gaussian Process	74.8 ± 1.7	0.0	1.0	51 ± 0.8
	Linear SVM	96.4 ± 3.1	93.9 ± 9.3	97.2 ± 3.7	99.5 ± 0.8
	Naive Bayes	82.3 ± 5	31.6 ± 18.5	99.4 ± 0.5	65.5 ± 9.2
	Nearest Neighbors	89.4 ± 4.6	64.1 ± 18.1	97.8 ± 2.9	95.4 ± 4.2
	Neural Net	93.2 ± 5.7	80.7 ± 24.1	97.4 ± 2.9	98.9 ± 1.2
	RBF SVM	75 ± 1.7	0.0	1.0	51 ± 1.1
	Random Forest	78.9 ± 4.3	20 ± 15.5	98.5 ± 2.6	82.3 ± 9.3
InceptionResNetV2	AdaBoost	89.5 ± 5.6	73.8 ± 17.8	94.8 ± 5.2	94.9 ± 5.4
	Gaussian Process	74.8 ± 1.7	0.0	1.0	51 ± 1.1
	Linear SVM	98 ± 3.2	96.3 ± 7.8	98.5 ± 3.5	99.8 ± 0.6
	Naive Bayes	75.3 ± 2	1.1	1.0	50.5 ± 1.8
	Nearest Neighbors	89.9 ± 5.4	62.2 ± 20.3	99.3 ± 2.3	96 ± 4.7
	Neural Net	89.9 ± 12.6	72.7 ± 24.6	95.6 ± 17.1	97.5 ± 8.5
	RBF SVM	75.1 ± 1.7	0.0	1.0	51 ± 1.1
	Random Forest	77.6 ± 4.3	13.9 ± 13.8	98.9 ± 2.1	80.6 ± 9.7
ResNet50	AdaBoost	92.6 ± 6.1	84.4 ± 17.1	95.3 ± 5.5	97.6 ± 4.1
	Gaussian Process	75.1 ± 1.6	0.0	1.0	51 ± 1.1
	Linear SVM	98.6 ± 2.1	99.9 ± 1.2	98.2 ± 2.8	99.7 ± 0.6
	Naive Bayes	77.8 ± 3.9	12.5 ± 12.5	1.0	56.3 ± 6.7
	Nearest Neighbors	94 ± 4.4	78.4 ± 17.7	99.2 ± 2.6	97.7 ± 4.9
	Neural Net	92.5 ± 6.4	85.5 ± 24.1	94.8 ± 7.3	98 ± 3.2
	RBF SVM	75 ± 1.7	0.0	1.0	51 ± 1.1
	Random Forest	76.3 ± 3.3	80 ± 10.8	99.1 ± 2	80.8 ± 9.6
VGG16	AdaBoost	89.9 ± 5.5	85.9 ± 17.9	94.9 ± 4.8	94.1 ± 6.8
	Gaussian Process	74.1 ± 1.7	0.0	1.0	51 ± 1
	Linear SVM	96.6 ± 3.4	98.8 ± 9.9	98.3 ± 3.1	99.6 ± 0.7
	Naive Bayes	90.2 ± 5.1	70.8 ± 20.3	99.3 ± 1.6	75 ± 10
	Nearest Neighbors	89.3 ± 4.5	57.3 ± 7	98.9 ± 3	83.9 ± 8.3
	Neural Net	94.3 ± 6.1	98.8 ± 25.3	97.5 ± 5.7	98.3 ± 2.1
	RBF SVM	76.1 ± 1.7	0.0	1.0	51 ± 1
	Random Forest	76.5 ± 3.1	13.2 ± 10.2	99.5 ± 1.6	82.8 ± 9.3