TABLE II. Performance Comparison of Four Architectures and Eight Classifiers (32 Combinations) for CT Images. All Values are Given in Percent.

Model	Classifier	Accuracy	Sensitivity	Specificity	AUC
DenseNet121	AdaBoost	78.9 ± 7.6	76.5 ± 11.5	80.1 ± 8.4	81.9 ± 5.8
	Gaussian Process	53.1 ± 10.9	0.0	1.0	0.5 ± 0.9
	Linear SVM	85.9 ± 5.9	84.9 ± 8.4	86.8 ± 6.3	93.1 ± 3.4
	Naive Bayes	74.9 ± 5.5	72.4 ± 24.4	77 ± 11.5	77 ± 9.1
	Nearest Neighbors	79.8 ± 6	69.7 ± 13.5	88.8 ± 5.3	86.8 ± 5.4
	Neural Net	83.4 ± 14.4	82 ± 28	84.6 ± 24	92.7 ± 13.4
	RBF SVM	53.1 ± 2.3	0.0	1.0	57 ± 3.1
	Random Forest	62.3 ± 9.2	46.2 ± 17.7	76.3 ± 11.7	67.4 ± 10.3
InceptionResNetV2	AdaBoost	75.2 ± 8.3	72.6 ± 12.8	77.5 ± 9.7	82.7 ± 7.4
	Gaussian Process	53.1 ± 10.9	0.0	1.0	0.5 ± 0.4
	Linear SVM	84.3 ± 7.3	83.2 ± 9	91.9 ± 7.4	91.9 ± 4.1
	Naive Bayes	75.7 ± 2.5	76.6 ± 3.8	75 ± 12.8	77.3 ± 13.6
	Nearest Neighbors	75.8 ± 8.2	57.3 ± 14.9	92 ± 4.4	84.4 ± 6.8
	Neural Net	80.7 ± 16.5	79.4 ± 28.1	82.6 ± 29	87.8 ± 15.6
	RBF SVM	53.1 ± 11	0.0	1.0	55 ± 2.4
	Random Forest	59.1 ± 9.9	42.3 ± 18	73.4 ± 13	68.7 ± 11.6
ResNet50	AdaBoost	76.1 ± 9.4	73.9 ± 13.5	78 ± 9.8	83.9 ± 7.6
	Gaussian Process	53.1 ± 11	0.0	1.0	0.5 ± 0.4
	Linear SVM	87.9 ± 5.8	86.5 ± 7.1	89.1 ± 5.4	94.2 ± 2.9
	Naive Bayes	59.5 ± 9.8	70.1 ± 30.6	50 ± 25.4	60.1 ± 5.5
	Nearest Neighbors	78.9 ± 8.2	65.2 ± 14.4	91.1 ± 4.8	87.2 ± 6.4
	Neural Net	84.6 ± 16.4	83.1 ± 30.4	88.3 ± 27.9	91.7 ± 16.7
	RBF SVM	53.1 ± 11	0.0	1.0	51 ± 0.8
	Random Forest	59.3 ± 9.1	35.1 ± 16.4	80.6 ± 10.1	63.3 ± 11.4
VGG16	AdaBoost	76.8 ± 7.8	73.9 ± 13.1	79.2 ± 8.9	84.2 ± 7.1
	Gaussian Process	53.1 ± 11	0.0	1.0	0.5 ± 0.4
	Linear SVM	86.5 ± 5.8	84.8 ± 8.2	88.1 ± 5.9	93.3 ± 3.3
	Naive Bayes	64.2 ± 12.2	74.5 ± 18.9	55 ± 22.5	64.8 ± 8.9
	Nearest Neighbors	71.4 ± 6.9	42.3 ± 14	97.1 ± 2.5	83.8 ± 6.2
	Neural Net	84.6 ± 16.3	85.8 ± 32.1	87.6 ± 27	92.8 ± 18.6
	RBF SVM	53.1 ± 11	0.0	1.0	0.5 ± 0.4
	Random Forest	59.7 ± 9	34.4 ± 16.4	82 ± 9.5	64.2 ± 11.8