Table 4.

Comparison of classification performance between the MVT-OFML model and other mainstream methods on the BreakHis dataset.

Backbone network or method	Year of publication	Magnification/times	Accuracy/%	Accuracy/%	Recall rate/%	F1 score/%
Deep-Net (11)	2020	40	94.43	95.25	95.55	95.39
		100	94.45	94.64	94.64	94.42
		200	92.27	90.71	92.24	91.42
		400	91.15	90.74	91.09	90.75
AnoGAN (21)	2021	40	99.15	99.64	99.46	99.78
		100	97.09	98.07	98.49	98.22
		200	87.58	88. 19	92.82	90.62
		400	87.3	82.77	92.5	88.23
BHC-Net (22)	2022	40	94.71	95.25	95.55	95.39
		100	94.6	94.51	94.64	94.42
		200	92.35	90.71	92.24	91.42
		400	91.5	90.74	91.09	90.75
BreaST-Net (23)	2022	40	96	—	—	95.8
		100	92.6	—	—	92.4
		200	93.5	—	—	93.6
		400	91.5	—	98.88	93.2
MVT-OFML	2023	40	99.19	98.93	98.9	98.46
		100	99.05	97.44	99.3	97.77
		200	99.6	97.88	99.54	99.33
		400	99.63	96.19	—	98.45
Improvement		40	3.19↑	—	—	2.66↑
		100	6.45↑	—	—	5.37↑
		200	6.10↑	—	—	5.73↑
		400	8.31↑	—		5.25↑

The symbol “↑”defines the improved values.