# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 4 Results and Discussion
### Comparison Results  
To assess the overall performance and generalizability of the proposed hybrid optimization framework, we conducted a comparative analysis across three datasets: the MIAS dataset, the Breast Ultrasound Images dataset, and the Fusion dataset that combines both sources. This comprehensive evaluation enables a deeper understanding of how different models, particularly the baseline MobileNet and its optimized variants using Firefly Law Algorithm FLA and DOA, perform across diverse imaging conditions and modalities. Figure 21 demonstrates an overall comparative evaluation of several deep learning models on three benchmark datasets: MIAS (mammographic images), the Image dataset of breast ultrasound, and the combined dataset that includes a combination of both modalities. The figure uses a grouped bar chart with color coding to identify an area of confusion because visual clarity is enhanced by using green to represent the MIAS dataset, light blue to represent ultrasound, and steel blue to represent the fusion dataset. Optimization of the mobileNet designs, with the use of metaheuristic techniques (FLA and DOA), were the best-performing designs in terms of achieving better accuracy for all datasets. The FLA-optimized MobileNet achieved a 98.06% accuracy on the MIAS dataset compared with its DOA version (94.17%) and the original MobileNet (92.93%). Regarding the ultrasound dataset, the same FLA version performed better with 95.44%, followed by DOA (94.22%), which is significantly better than other conventional architectures such as VGG16, ResNet50, or CNN. The best performance was noted on the fusion dataset, where the DOA-optimized MobileNet outperformed the FLA-enhanced one with an MAE of 98.96 percent compared to its 98.70 percent, respectively. These findings show that hybrid techniques of optimization have high levels of resilience and applicability in heterogeneous medical data. The stable high results on all the datasets confirm the effectiveness of the mobile-based frameworks, especially after adding bio-inspired optimization strategies, to improve the accuracy of breast cancer classification. These findings confirm that opti  
Figure 20: Grad-CAM visualization on a fused mammographic–ultrasound image using the Optimized MobileNet-DOA model. The heatmap demonstrates focused activation in the lesion area, illustrating the model’s ability to leverage multimodal features for accurate malignancy prediction. mization techniques significantly enhance model performance, with the Fusion dataset delivering the highest accuracies due to the rich, diverse features it contains, as presented in Table 11. The DOA-optimized MobileNet model, in particular, proved most effective under these complex conditions. According to Table 12, a thorough comparative study was performed to compare the suggested hybrid optimization framework with the breast cancer detection methodologies involving ensemble learning, transfer learning, explainable AI, and metaheuristic optimization. As one of the notable works, [PERSON] et al. [34] exploit an ensemble of pretrained CNNs to detect micro calcification with 93.05% accuracy; [PERSON] et al. [36] utilize an ensemble of pretrained CNNs with the idea of few-shot learning combined with transfer learning, where they report 95.6% and 0.970 AUC; [PERSON] et al. [37] make use of attention-guided Grad-CAM and an ensemble of CNNs to detect microcalcification, with the results of 9 A simplified CNN provided 94.23% and 96.67% on MIAS and fusion dataset, respectively [42]; and ResNet50, with the Improved Marine Predators Algorithm, performed skillfully at 98.88% [49]. [PERSON] et al. [51] incorporated DeepLabV3+, autoencoders, and Grad-CAM and achieved 97.4% on ultrasound images. Our optimized MobileNet models performed competitively or better in comparison when tested with MIAS with FLA (98.06% accuracy,