# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 5 Conclusions  
Breast cancer remains one of the most critical public health challenges globally, necessitating the advancement of accurate and interpretable diagnostic methodologies. This study introduced an explainable deep learning framework for breast cancer classification, integrating the computational efficiency of the MobileNet architecture with the optimization capabilities of two bio-inspired algorithms: the DOA and the FLA. The proposed approach follows a systematic pipeline--initial model selection, integration of metaheuristic optimization, and multimodal evaluation--aimed at enhancing diagnostic accuracy while preserving model transparency. Extensive experiments were conducted on three benchmark datasets: the MIAS mammography dataset, the Breast Ultrasound Images dataset, and a combined Fusion dataset. The FLA-optimized MobileNet achieved superior results on the MIAS dataset, with an accuracy of 98.06%, precision, recall, and F1-score all reaching 0.98, and an AUC of 0.9982, indicating excellent class separability. On the ultrasound dataset, the same model attained an accuracy of 95.44%, with F1-scores of 0.97 and 0.93 for benign and malignant classes, respectively. On the multimodal Fusion dataset, the DOA-optimized MobileNet outperformed all models, achieving an accuracy of 98.96%, and precision, recall, and F1-score values of 0.98, demonstrating robust generalization across diverse imaging modalities. Future research directions include expanding the framework to incorporate additional imaging modalities such as MRI and thermographic scans, integrating attention-based mechanisms to further enhance interpretability, and implementing and validating the system in real-time clinical settings to assess its practical diagnostic utility. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Model/Method** & **Dataset** & **Results** \\ \hline \multirow{3}{*}{[PERSON] et al., 2024 [34]} & Ensemble (AlexNet, GoogLeNet, & & \\  & VGG16, ResNet-50), & Mammograms & 93.05\% (microcalcifications), \\  & Transfer Learning & & 88.59\% (normal) \\ \hline [PERSON] et al., 2025 [36] & Few-Shot Learning + Transfer Learning (Relation Network) & Mammograms (Few-shot) & Accuracy: 95.6\%, AUC: 0.970 \\ \hline \multirow{2}{*}{[PERSON] et al., 2024 [37]} & Attention-Guided Grad-CAM+En & Infrared Thermograms (DMR) & Accuracy: 98.04\%, AUC: 0.97 \\  & sample CNNs & & \\ \hline [PERSON] et al., 2024 [42] & Custom Simplified CNN & MIAS, DDSM, Fusion & Accuracy: 94.23\%(MIAS), \\  & & & 95.53\% (DDSM), \\  & & & 96.67\% (Fusion) \\ \hline [PERSON] et al., 2022 [49] & ResNet50 + Improved Marine Predators Algorithm (IMPA) & MIAS, CBIS-DDSM & Accuracy: up to 98.88\% \\ \hline [PERSON] et al., 2025 [51] & DeepLabV3++ Autoencoder + Grad-CAM (XAI) & Breast Ultrasound & Accuracy: 97.4\%, Dice: 0.981 \\ \hline \multirow{2}{*}{**Proposed Method**} & **Optimized MobileNet + FLA** & **MIAS Dataset** & **Precision/Recall/F1: 0.98** \\  & & & **Accuracy: 98.06\%** \\ \hline \multirow{2}{*}{**Proposed Method**} & **Optimized MobileNet + FLA** & **Breast Ultrasound Images** & **Accuracy: 95.44\%,** \\  & & & **Precision/Recall/F1: 0.95** \\ \hline \multirow{2}{*}{**Proposed Method**} & **Optimized MobileNet + DOA** & **Fusion Dataset** & **Accuracy: 98.96\%** \\  & & & **Precision/Recall/F1: 0.98** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparative Summary Table of Related Works and Proposed Method. **Author Contributions:** The first author, [PERSON], conducted experiments, wrote the manuscript, and executed the software process. The second author, [PERSON], was responsible for supervision and correcting the direction of the work. The third author, [PERSON], was responsible for project administration and writing--review and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.