# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 4 Results and Discussion
### Performance of Hybrid Models in the Fusion Dataset  
The last stage of evaluation utilizes the Fusion Dataset, which unifies the MIAS mammographic set and Breast Ultrasound Images to bring more heterogeneity and resemble a more closely real-life diagnostic setting. Multimodal data make models hard to generalize across imaging variability. MobileNet also proved to be the strongest in all the metrics of accuracy (91.95%), precision (0.92), recall (0.92), and F1-score (0.92), as it is also lightweight in structure and the feature extraction is consequently effective due to depthwise separable convolutions (see Table 8). DenseNet and Xception also turned out  
Figure 14: AUC-ROC Curve of the FLA-Optimized MobileNet. to be competitive, having scaled 85.91 and 85.41 percentages of accuracy, respectively, but they were not as balanced or consistent as MobileNet. VGG16 performed better (75.22% accuracy), whereas ResNet50, CNN, and EfficientNet were way behind, with EfficientNet performing particularly poorly in terms of precision (0.32), indicating a high ratio of false positives. The results confirm that MobileNet is effective and reliable in multimodal breast cancer classification, providing support for its potential application in real life concerning various diagnostic facilities. To enhance classification performance, the MobileNet architecture was optimized using two bio-inspired metaheuristic algorithms: the DOA and FLA. The confusion matrix for the Optimized MobileNet with DOA is displayed in Figure 15. Only 5 benign and 10 malignant samples were misclassified, while 443 benign and 337 malignant samples were properly predicted by the model. This model obtained a test accuracy of 98.96%, with precision, recall, and F1-score values of 0.98 or higher for both classes, as shown in Table 9. The DOA-enhanced model is well-suited for real-world diagnostic applications where input variability is widespread, since these results show that it performs robust and highly accurate classification on heterogeneous data. In comparison, the performance of the Optimized MobileNet with FLA is shown in Figure 16. This model also exhibited strong classification ability, correctly identifying 443 benign and 336 malignant samples, while misclassifying 5 benign and 11 malignant cases. The accuracy is 98.70%, and the precision, recall, and F1-score values for both categories are consistently near 0.98, according to the relevant classification report in Table 10. While this performance is outstanding, it is slightly lower than the DOA-optimized counterpart in terms of overall accuracy and true positive predictions for malignant cases. Based on the comparative summary, the DOA-optimized MobileNet slightly outperformed the FLA-optimized version in all performance indicators. The final comparison study revealed that, in contrast to the FLA model's accuracy of 98.70%, the DOA model had the greatest overall accuracy (98.96%), precision (0.98), recall (0.98), and F1-score (0.98). These findings confirm that the use of the DOA provided a marginal yet valuable advantage in optimizing the MobileNet architecture for fusion-based breast cancer classification. This  
Figure 16: Confusion Matrix of the Optimized MobileNet with FLA on the Fusion Dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline Benign & 0.99 & 0.99 & 0.98 \\ Malignant & 0.97 & 0.97 & 0.97 \\ Overall & 0.98 & 0.98 & 0.98 \\
**Accuracy** & & **0.9870** & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification Report for the Fusion Dataset Using the Optimized MobileNet with FLA.  
demonstrates the potential of DOA as a superior optimization strategy when applied to complex multi-source medical image datasets. Figure 17 illustrates the AUC-ROC curve for the DOA-Optimized MobileNet model applied to the fusion dataset. The curve demonstrates a strong classification performance, with the model achieving an Area Under the Curve (AUC) of 0.9940.