Table 3 Comparison of the suggested BC detection framework with conventional classifiers. (Unit:%).
From: Intelligent breast cancer diagnosis with two-stage using mammogram images
TERMS/Classifiers | KNN29 | CNN25 | XGBoost24 | ACA-ATRUNet-MDN40 | MML-EOO-ACA-ATRUNet-MDN |
|---|---|---|---|---|---|
MIAS Mammography Dataset | |||||
NPV | 83.607 | 87.435 | 87.368 | 87.568 | 93.434 |
Accuracy | 73.913 | 80.124 | 79.814 | 78.882 | 89.130 |
F1-Score | 66.929 | 73.984 | 73.684 | 73.016 | 85.356 |
Specificity | 73.913 | 80.676 | 80.193 | 78.261 | 89.372 |
Precision | 61.151 | 69.466 | 68.939 | 67.153 | 82.258 |
FPR | 26.087 | 19.324 | 19.807 | 21.739 | 10.628 |
MCC | 0.463 | 0.583 | 0.578 | 0.565 | 0.769 |
Sensitivity | 73.913 | 79.130 | 79.130 | 80.000 | 88.696 |
FDR | 38.849 | 30.534 | 31.061 | 32.847 | 17.742 |
FNR | 26.087 | 20.870 | 20.870 | 20.000 | 11.304 |
CBIS-DDSM Breast Cancer Image | |||||
FPR | 25.254 | 20.069 | 21.593 | 20.176 | 10.877 |
Sensitivity | 74.185 | 79.583 | 78.087 | 79.904 | 88.936 |
Accuracy | 74.559 | 79.815 | 78.300 | 79.850 | 89.061 |
NPV | 85.274 | 88.675 | 87.739 | 88.820 | 94.156 |
Specificity | 74.746 | 79.931 | 78.407 | 79.824 | 89.123 |
Precision | 59.494 | 66.473 | 64.390 | 66.444 | 80.348 |
F1-Score | 66.032 | 72.440 | 70.580 | 72.555 | 84.424 |
FNR | 25.815 | 20.417 | 21.913 | 20.096 | 11.064 |
MCC | 0.468 | 0.573 | 0.543 | 0.575 | 0.763 |
FDR | 40.506 | 33.527 | 35.610 | 33.556 | 19.652 |
