BMC Bioinformatics

Table 6 Performance comparison of each model

From: CRANet: a comprehensive residual attention network for intracranial aneurysm image classification

Models	2-classification			4-classification
	Accuracy	Recall	F1	Accuracy	Recall	F1
ResNet18	96.57	0.91	0.92	91.75	0.87	0.88
ResNet34	96.10	0.70	0.73	90.25	0.88	0.87
ResNet50	95.64	0.84	0.86	89.15	0.85	0.85
ResNet101	95.23	0.89	0.91	89.60	0.85	0.84
VGG	96.64	0.89	0.88	90.85	0.85	0.86
GoogleNet	96.87	0.89	0.89	86.50	0.81	0.79
InceptionV3	97.16	0.91	0.92	91.68	0.87	0.86
DenseNet	96.32	0.89	0.87	88.79	0.84	0.84
CNN	95.89	0.87	0.85	86.27	0.82	0.81
CRANet (Our model)	97.81	0.94	0.94	92.55	0.91	0.91

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ISSN: 1471-2105

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