Table 3.

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

Backbone network or method	Year of publication	Magnification/times	Accuracy/%	Accuracy/%	Recall rate/%	F1 score/%
IDSNet (20)	2020	40	91.5	90.55	91	90.54
		100	90.4	91.23	90.56	90.78
		200	95.3	95.33	95.66	95.39
		400	86.7	89.35	88.42	89.47
DCET-Net (18)	2021	40	99	99.47	97.38	98.41
		100	98.08	94.79	98.91	96.81
		200	99.34	97.66	97.82	98.82
		400	98.72	98.22	97.65	97.93
RANet-ADSVM (13)	2022	40	91.96	93.83	94.91	94.36
		100	96.83	98.52	98.3	98.32
		200	98.05	98.92	99.15	99.13
		400	90.3	93.17	93.56	93.35
VIT-DeiT (17)	2022	40	99.43	99.38	99.46	99.4
		100	98.34	98.31	98.51	98.35
		200	98.27	98.32	98.27	98.23
		400	98.82	98.57	98.78	98.65
MVT-OFML	2023	40	99.77	99.75	99.71	99.77
		100	99.56	99.74	99.54	99.44
		200	99.76	99.65	99.43	99.62
		400	99.45	99.3	99.69	99.33
Improvement		40	0.34↑	0.37↑	0.25↑	0.37↑
		100	1.22↑	1.43↑	1.03↑	1.09↑
		200	1.40↑	1.33↑	1.16↑	1.39↑
		400	0.63↑	0.73↑	0.96↑	0.91↑

The symbol “↑”defines the improved values.