# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 4 Results and Discussion
### Performance of Hybrid Models in the Breast Ultrasound Images Dataset  
The second community assessment was conducted on the Breast Ultrasound Images Dataset, which is known to be complex to diagnose due to noise, low contrast, and variation in the images. Table 5 shows that MobileNet performed the best of all models, indicating the highest level of accuracy (93.53%), with all other measures, F1-score (0.8982) and recall (0.8929), also performing well, signifying that it was the most suitable model in processing ultrasound information. DenseNet and Xception demonstrated competitive results as well, with the former displaying an excellent precision (0.9221) but poor recall and the latter performing similarly in accuracy (90.49%) but different in precision-recall trade-offs. Although ResNet50 has a high accuracy (94.12%), it's very low recall (0.3809), however, is of significant concern since this leads to a high percentage of false negatives. Custom CNN and EfficientNet resulted in the lowest performance, especially on recall and F1-score. In  
Figure 11: AUC-ROC of the FLA-Optimized MobileNet. \begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline Benign & 0.98 & 0.98 & 0.98 \\ Malignant & 0.98 & 0.98 & 0.98 \\ Overall & 0.98 & 0.98 & 0.98 \\
**Accuracy** & & **0.9806** & \\ \hline \end{tabular}
\end{table} 
Table 4: Assessment of Various Models’ Performance on the MIAS Dataset. general, these results indicate the versatile and stable behavior of MobileNet in the realm of ultrasound-based breast cancer detection because of its moderately high results in the most important indexes of effectiveness. In order to assess how well the hybrid optimization techniques performed on the Breast Ultrasound Images Dataset, two improved models were created by combining the MobileNet architecture with FLA and DOA, respectively. These optimizers were employed to improve the network's parameter configuration and convergence behavior, aiming for greater diagnostic accuracy and generalizability. Figure 12 shows the confusion matrix of the Optimized MobileNet with DOA, where the model correctly classified 170 benign and 77 malignant cases, while misclassifying 9 benign and 7 malignant instances. The corresponding classification report in Table 6 indicates a test accuracy of 94.22%. All three metrics--precision, recall, and F1-score--were 0.95 for the benign class and 0.91 for the malignant class. The accuracy, recall, and F1-score averages were all 0.93, indicating a good trade-off between sensitivity and specificity. This demonstrates that the DOA-assisted model is effective at distinguishing between cancerous and non-cancerous ultrasound images, though with slightly more errors in identifying malignant cases. In comparison, Figure 13 presents the confusion matrix of the Optimized MobileNet with FLA, which further improves classification accuracy. Only 5 benign and 7 malignant cases were incorrectly classified by the model, whereas 174 benign and 77 malignant cases were accurately predicted. With precision, recall, and F1-score values of 0.96, 0.97, and 0.97 for benign cases and 0.94, 0.92, and 0.93 for malignant instances, the test accuracy was 95.44%, as shown in Table 7. A well-balanced and extremely dependable model that could reduce both false positives and false negatives was demonstrated by the overall average scores of 0.95 for all criteria. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline Benign & 0.95 & 0.95 & 0.95 \\ Malignant & 0.91 & 0.91 & 0.91 \\ Overall & 0.93 & 0.93 & 0.93 \\
**Accuracy** & & **0.9422** & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification Report of Optimized MobileNet with DOA on the Breast Ultrasound Images Dataset. Figure 13: Confusion Matrix of Optimized MobileNet with FLA on the Breast Ultrasound Images Dataset. The algorithm correctly predicted 174 benign and 77 malignant cases, whereas only five benign and seven malignant cases were misclassified.