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.