## 4 Results  
In the literature on deep learning methods for medical imaging, it was noticed that the most common methods for breast cancer were CNN architectures, most notably VGG-16, Alexnet, and Inception V3. The VGG-16 and Inception architectures are often utilized with the help of transfer learning. The experiment showed promising results that went from 80% accuracy to 95%. Table 2 shows that the results obtained in the training and developing sets, using the balanced DDSM_YERAL data, are similar to those shown in other articles. However, in the test section where we use the YERAL validation set, the accuracy and all the other metrics have a more significant disparity. Table 2 also shows the loss on training and developing sets. These results highlight that the behavior of the training was expected and did not overfit the data. After these results, there was a revision of the other parameters that could have potentially caused the low accuracy in the test set. Specialists from the FUCAM confirmed that the YERAL data set had no issue and that all images were labeled correctly. Moreover, comparing the hardware requirements from the few articles that had them, they were not far from the ones used in this work. In the Background section, we divided CNN architectures into two broad categories. The first is the shallow and linear models, typified by Alexnet and VGG-16. The second encompasses the deeper model types like Inception and other architectures, which incorporate built-in modules, making them inherently more intricate. A close inspection of Table 2 reveals that the VGG-16, when incorporated with transfer learning, stands out by maintaining only a nominal gap between the accuracy of the developing and test sets. Contrarily, the Inception architecture, while registering impressive metrics on the training and developing sets, unfortunately fails when it comes to the test set. This pattern suggests that while deeper networks have their merits, they might not always be the optimal choice, and conventional ML algorithms still hold substantial value. Moreover, the interrelation between Table 2 and Figure 4 provides deeper insights into the training process. The training was executed on the balanced data set amalgamating the DDSM and YERAL sets. Despite the training and developing sets exhibiting standard accuracy and loss rates as shown in Table 2, the test set from the YERAL data set depicted in Figure 4 does not quite match up to the anticipated accuracy outcomes. Furthermore, Figure 4 delineates the training trajectory for a range of popular CNN architectures. Remarkably, both models rooted in VGG-16 (including its transfer learning counterpart) display the steadiest results. A noteworthy difference between them lies in the training duration. The standalone VGG-16 model took roughly 45 min per epoch, whereas its transfer learning variant achieved the same in under 10 min. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**DDSM\_YERAL**} & \multicolumn{4}{c}{**YERAL**} \\ \hline  & & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Developing Set**} & \multicolumn{4}{c}{**Test Set**} \\ \hline  & Model & Loss & Acc & Loss & Acc & Acc & Prec & Rec & F1 \\ \hline
1 & Alexnet & 0.36 & 82\% & 0.31 & 84\% & 60\% & 57\% & 76\% & 65\% \\
2 & VGG-16 & 0.39 & 79\% & 0.35 & 82\% & 52\% & 51\% & 90\% & 65\% \\
3 & VGG-16 TF & 0.25 & 87\% & 0.28 & 87\% & 79\% & 82\% & 74\% & 77\% \\
4 & Inception & 0.35 & 81\% & 0.38 & 83\% & 57\% & 53\% & 93\% & 69\% \\
5 & Inception TF & 0.39 & 80\% & 0.55 & 71\% & 55\% & 52\% & 90\% & 66\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of popular CNN architectures. Rows 1, 2, and 4 represent the architecture introduced in the literature. Rows 3 and 5 represent the architectures combined with transfer learning, keeping the last pooling layers unfrozen. The gray highlighted section shows the accuracy of our target data set and the metric we seek to improve.