Computational Intelligence and Healthcare Informatics. Группа авторов

- - - ResNet50 0.707 0.814 0.736 0.613 0.561 0.716 0.63 0.789 - - - - - - [41] 0.762 0.883 0.816 0.679 0.801 0.729 0.709 0.838 0.744 0.841 0.884 0.800 0.754 0.876 [49] 0.862 0.831 0.901 0.721 0.909 0.894 0.851 0.944 0.893 0.924 0.704 0.806 0.798 0.851 [13] - 0.875 0.962 - - - - 0.861 0.850 0.868 - - - -

Table 2.4 Comparison of DL models on the basis of different performance metrics.

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Ref.	Model	Accuracy	F1-Score	Specificity	Sensitivity	PPV	NPV	AUC	Recall	Precision
[23]	DenseNet121	0.572–0.842	0.574 –0.942	-	-	-	-	-	-	-
[67]	AlexNet (S)	0.9684	0.9023	87.99	92.65	87.94	90.68	-	-	-
	VGG16 (S)	0.9742	0.9228	91.46	93.42	91.18	93.63	-	-	-
	VGG19 (S)	0.9757	0.9161	88.86	94.49	88.90	94.46	-	-	-
	DenseNet121(S)	0.9801	0.9248	90.01	95.10	90.00	95.11	-	-	-
	ResNet18 (S)	0.9766	0.9099	85.09	96.63	85.97	96.52	-	-	-
	Inceptionv3(S)	0.9796	0.9225	89.58	95.08	89.58	95.08	-	-	-
	ResNet50 (S)	0.9775	0.9233	90.59	94.32	90.43	94.42	-	-	-
[7]	VDSNet	0.73