Table 4 Algorithmic evaluation of the recommended BC detection model. (Unit:%).
From: Intelligent breast cancer diagnosis with two-stage using mammogram images
TERMS/Algorithm | GWO-ACA-ATRUNet-MDN36 | HBA-ACA-ATRUNet-MDN37 | JAYA-ACA-ATRUNet-MDN38 | EOO-ACA-ATRUNet-MDN39 | MML-EOO-ACA-ATRUNet-MDN |
|---|---|---|---|---|---|
MIAS mammography dataset | |||||
FPR | 14.493 | 14.493 | 13.527 | 13.043 | 10.628 |
FDR | 23.438 | 23.256 | 22.222 | 21.429 | 17.742 |
Sensitivity | 85.217 | 86.087 | 85.217 | 86.087 | 88.696 |
NPV | 91.237 | 91.710 | 91.327 | 91.837 | 93.434 |
Precision | 76.563 | 76.744 | 77.778 | 78.571 | 82.258 |
Accuracy | 85.404 | 85.714 | 86.025 | 86.646 | 89.130 |
FNR | 14.783 | 13.913 | 14.783 | 13.913 | 11.304 |
Specificity | 85.507 | 85.507 | 86.473 | 86.957 | 89.372 |
F1-Score | 80.658 | 81.148 | 81.328 | 82.158 | 85.356 |
MCC | 0.692 | 0.700 | 0.704 | 0.717 | 0.769 |
CBIS-DDSM breast cancer image | |||||
Specificity | 86.291 | 86.104 | 86.317 | 87.146 | 89.123 |
NPV | 92.363 | 92.639 | 92.603 | 93.039 | 94.156 |
MCC | 0.700 | 0.703 | 0.705 | 0.721 | 0.763 |
Precision | 75.768 | 75.644 | 75.906 | 77.182 | 80.348 |
FPR | 13.709 | 13.896 | 13.683 | 12.854 | 10.877 |
Accuracy | 86.104 | 86.175 | 86.282 | 87.084 | 89.061 |
FDR | 24.232 | 24.356 | 24.094 | 22.818 | 19.652 |
F1-Score | 80.441 | 80.629 | 80.731 | 81.779 | 84.424 |
FNR | 14.270 | 13.683 | 13.789 | 13.041 | 11.064 |
Sensitivity | 85.730 | 86.317 | 86.211 | 86.959 | 88.936 |
