# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 4 Results and Discussion
### Performance of Hybrid Models in the MIAS Dataset  
The MIAS dataset has been applied as an initial benchmark to compare the classification performance of different types of deep learning models within the scope of the mammographic images due to its diversity in terms of imaging conditions as well as pathological cases. Table 2 indicates that MobileNet showed the best results with the highest accuracy (93.76%) and equal precision, recall, and F1-score (0.9376), which mainly proves its high efficiency when it comes to feature extraction despite having few data. Xception and DenseNet were the next similar architectures with robust but slightly less accuracy (88.9% and 88.4%, respectively), though, such models as VGG16, ResNet50, EfficientNet, and the custom CNN fared so badly that the overall accuracy of VGG16 and ResNet50 made only 54.09% each. These results state the security and applicability of MobileNet on mammographic image classification and place it as an encouraging selection to be progressed and used in practice further.  
To further improve the classification performance on the MIAS dataset, two hybrid models were developed by integrating optimization algorithms with the MobileNet architecture: one using the DOA and the other employing the FLA. These bio-inspired optimizers were used to enhance the model's parameter tuning and convergence behavior. The evaluation of both optimized models was conducted using standard classification metrics, supported by confusion matrices and classification reports.  
The confusion matrix of the MobileNet model optimized with DOA is shown in Figure 9. It reveals that 370 benign and 309 malignant cases were properly categorized, whereas 20 benign and 22 malignant instances were misclassified. Table 3 displays the relevant classification report, which indicates a test accuracy of 94.17% with precision, recall, and F1-score values all centered around 0.94. These outcomes demonstrate how well DOA directs the optimization process, enabling the model to continue delivering accurate and balanced performance across both classes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline MobileNet & 0.937587 & 0.937604 & 0.937587 & 0.937594 \\ Xception & 0.889043 & 0.889281 & 0.889043 & 0.888823 \\ DenseNet & 0.884882 & 0.885869 & 0.884882 & 0.884459 \\ CNN & 0.610264 & 0.610927 & 0.610264 & 0.610538 \\ EfficientNet & 0.540915 & 0.292589 & 0.540915 & 0.379761 \\ VGG16 & 0.540915 & 0.292589 & 0.540915 & 0.379761 \\ ResNet50 & 0.540915 & 0.292589 & 0.540915 & 0.379761 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Assessment of Various Models’ Performance on the MIAS Dataset.  
The FLA-optimized model outperformed the DOA-optimized version in all metrics, confirming its superior ability to fine-tune the learning process. The fusion of MobileNet with FLA provided the highest level of diagnostic accuracy, making it a highly effective solution for breast cancer detection based on mammographic images.  
Figure 11 illustrates the AUC-ROC curve for the FLA-optimized MobileNet model applied to the MIAS dataset. The curve demonstrates excellent separability between the benign and malignant classes, with the model achieving an area under the curve (AUC) of 0.9982. This near-perfect AUC indicates a highly effective classifier with outstanding discriminative ability, where the true positive rate remains consistently high across a wide range of false positive rates. The curve's steep ascent and close proximity to the top-left corner of the plot reflect minimal trade-off between sensitivity and specificity, further validating the model's robustness and reliability in clinical breast cancer detection tasks.