Feature Fusion Based on Convolutional Neural Network for Breast Cancer Auxiliary Diagnosis

Table 2

Comparison of the results of various indicators under different magnifications.

Enlarge size	Model	Accuracy (%)	(%)	(%)	(%)
40x	VGG16	94.39	92.99	94.22	93.56
	InceptionV3	92.20	97.73	89.85	90.71
	ResNet50	84.15	81.76	85.70	82.79
	VIRNets (ours)	99.02	98.86	98.97	98.90
100X	VGG16	86.19	83.51	86.61	84.63
	InceptionV3	96.67	95.78	97.93	96.74
	ResNet50	90.95	88.64	91.75	89.86
	VIRNets (ours)	97.62	96.43	98.28	97.27
200X	VGG16	83.66	80.85	81.62	81.21
	InceptionV3	97.03	98.12	97.26	97.67
	ResNet50	91.58	91.43	88.68	89.88
	VIRNets (ours)	98.02	97.02	98.56	97.73
400X	VGG16	86.41	84.84	83.9	84.34
	InceptionV3	95.56	94.27	96.34	95.17
	ResNet50	83.15	80.73	82.77	81.50
	VIRNets (ours)	98.37	97.95	98.36	98.15