Table 3.

Performance assessments of different models.

Classifier	Measure	VGG16	VGG19	Resnet18	Resnet50	Resnet101	Xception	Densenet	Voting
Random Forest	Accuracy	0.4717	0.4528	0.5472	0.4528	0.3774	0.4906	0.4528	0.4528
	Sensitivity	0.9565	1.0000	0.8696	1.0000	0.8261	1.0000	1.0000	1.0000
	Specificity	0.1000	0.0333	0.3000	0.0333	0.0333	0.1000	0.0333	0.0333
	PPV	0.4490	0.4423	0.4878	0.4423	0.3958	0.4600	0.4423	0.4423
	NPV	0.7500	1.0000	0.7500	1.0000	0.2000	1.0000	1.0000	1.0000
	F1-score	0.4400	0.4423	0.4545	0.4423	0.3654	0.4600	0.4423	0.4423
K-nearest Neighbor	Accuracy	0.7925	0.6981	0.7547	0.7736	0.8679	0.7736	0.6415	0.8491
	Sensitivity	0.6957	0.6087	0.6957	0.6087	0.6957	0.6087	0.4783	0.6957
	Specificity	0.8667	0.7667	0.8000	0.9000	1.0000	0.9000	0.7667	0.9667
	PPV	0.8000	0.6667	0.7273	0.8235	1.0000	0.8235	0.6111	0.9412
	NPV	0.7879	0.7188	0.7742	0.7500	0.8108	0.7500	0.6571	0.8056
	F1-score	0.5926	0.4667	0.5517	0.5385	0.6957	0.5385	0.3667	0.6667
Support Vector Machine	Accuracy	0.7925	0.8868	0.8302	0.8679	0.8868	0.8491	0.9057	0.9245
	Sensitivity	0.7391	0.9130	0.8261	0.8696	0.8261	0.7391	0.9565	0.9565
	Specificity	0.8333	0.8667	0.8333	0.8667	0.9333	0.9333	0.8667	0.9000
	PPV	0.7727	0.8400	0.7917	0.8333	0.9048	0.8947	0.8462	0.8800
	NPV	0.8065	0.9286	0.8621	0.8966	0.8750	0.8235	0.9630	0.9643
	F1-score	0.6071	0.7778	0.6786	0.7407	0.7600	0.6800	0.8148	0.8462

Highest values are presented in bold.