## 5 Conclusions  
Deep learning continues to evolve rapidly, with the promise of even more advanced methodologies on the horizon. The core challenge in medical imaging is not just about the sophistication of models but about their applicability and robustness across diverse data sets. A significant opportunity in current research is the limited generalizability of many state-of-the-art models, which, while performing excellently on benchmark data sets, do not perform properly on real-world, practical data sets like YERAL. Addressing this gap was a central theme of our study. The computational science of medical imaging demands stringent standardization in experimentation and result representation. Furthermore, it is vital to distinguish between standard computational vision algorithms and medical imaging, recognizing their unique challenges. As demonstrated in this paper, the way forward may not necessarily lie in exclusively using the latest deep learning tools. Instead, our research emphasizes the potential of adapting and molding these tools to fit specific requirements. We have showcased that by integrating advanced deep learning techniques with what many consider a traditional approach, ensemble learning algorithms can achieve superior performance across diverse data sets. In our research journey, we initiated our experimentation by assessing the efficacy of the most prevalent methodologies in medical image classification. However, what set our work apart was our commitment to delivering an efficient and accurate tool tailored for specialists, particularly in light of the unique challenges presented by the YERAL data set. This dedication led us to revisit and repurpose certain strategies, which, although perceived as dated by contemporary standards, unveiled significant potential in enhancing the precision of modern techniques. Highlighting our findings, the bagging ensemble model, integrated with VGG-16 using transfer learning, emerged as a game changer, far surpassing the standalone performance of VGG-16 with transfer learning. What is equally noteworthy is the promising accuracy achieved by both the stacking model and the boosting algorithm. Their incorporation into the realm of image classification, especially when used with the potent feature extraction \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DDSM\_YERAL**} & \multicolumn{3}{c}{**YERAL**} \\  & \multicolumn{2}{c}{**Developing Set**} & \multicolumn{3}{c}{**Test Set**} & \\ \hline Model & Acc & Acc & Prec & Rec & F1 \\ \hline Alexnet\_Bagging & 84\% & 70\% & 67\% & 76\% & 71\% \\ VGG-16 TF\_Bagging & 91\% & 82\% & 79\% & 86\% & 82\% \\ \hline Stacking VGG-16 TF & 84\% & 78\% & 85\% & 78\% & 77\% \\ Stacking Inception TF & 82\% & 74\% & 78\% & 74\% & 73\% \\ \hline AdaBoost VGG-16 TF & 83\% & 76\% & 82\% & 76\% & 75\% \\ AdaBoost Inception TF & 80\% & 71\% & 75\% & 71\% & 70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the expected accuracy of the ensemble algorithms and the results of the test set predictions of that model. The highlighted gray area represents the accuracy of our test data set and it is where one can see the improvement from previous results. capabilities of transfer learning, suggests a paradigm shift, challenging conventional notions and offering renewed perspectives on the subject. The relationship between traditional machine learning algorithms and select deep learning methodologies has come as an unexpected yet potent combination, often outperforming the interest of more intricate networks. Notably, these more elaborate architectures present their own challenges, particularly when users find themselves with hardware limitations, insufficient training data, or difficulties typical of medical images. One hypothesis that our work highlights is the challenge of generalizing medical imaging problems solely using deep learning. The importance of this challenge often stems from the necessity of turning to benchmark data sets to boost data volumes.