Fig 1.
Proposed methodology.
Fig 2.
Sample images from the BreakHis dataset.
(a) Benign images, (b) Malignant images.
Fig 3.
CSCO’s flowchart.
Fig 4.
Hybrid feature selection algorithm of CSCO-ROA.
Fig 5.
Comparing the proposed CVAE design to a standard VAE framework.
Fig 6.
Proposed CVAE model.
Fig 7.
Accuracy vs. Epoch.
Fig 8.
Loss vs. Epoch.
Fig 9.
Segmentation results of the proposed model.
Table 1.
Features selected by optimization algorithms.
Fig 10.
Confusion matrix for BrC classification on BreakHis dataset.
Fig 11.
Performance of classification model with and without feature selection.
Table 2.
Performance of classification model using 5-fold cross-validation (before FS).
Table 3.
Performance of classification model using 5-fold cross-validation (after FS).
Fig 12.
Performance of breast cancer classification technique.
Table 4.
Performance metrics for BrC.
Table 5.
Compares the accuracy, dataset, and FPR of our suggested model with existing methods.
Fig 13.
Comparison of AUC values for BrC classification techniques.