## 5 Conclusions  
This research explored integrating Transformer and MBConv blocks into the DenseNet-201 neural network for classifying histopathological images of penile cancer. Our experiments demonstrated that some models trained from the created architectures achieved significant results, particularly in accuracy, precision, recall, and F1-score. Based on the analysis of these results, we identified several architectures with interesting outcomes, such as I + D + D + M (40\(\times\) and 100\(\times\)), I + D + M + M (100\(\times\)), and I + D + M + T (40\(\times\) and 100\(\times\)). Furthermore, the first and third architectures mentioned previously proved to be multi-scale solutions, performing well at both magnifications. Using the best architectures for each magnification, we analyzed the GRAD-CAM algorithm to identify the regions of the images that the neural network assigned more importance to, obtaining valuable  
Figure 8: This image shows the analysis result via GRAD-CAM for samples at 40\(\times\) and 100\(\times\) magnifications. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Works** & **Magnification** & **F1-Score** \\ \hline DenseNet-201 [11] & 40\(\times\) & 97.39\% (2.13) \\ DenseNet-201 [11] & 100\(\times\) & 97.31\% (3.62) \\ DenseNet-121 + SA [12] & 40\(\times\) & 91.2\% (2.3) \\ DenseNet-121 + SA [12] & 100\(\times\) & 92.4\% (1.5) \\ DenseUSAG [13] & 100\(\times\) & 93.1\% (4.6) \\ Ours (I + D + M + M) & 40\(\times\) & 95.78\% (4.39) \\ Ours (I + D + D + M) & 100\(\times\) & 94.99\% (3.88) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparative table of our work with others involves classifying histopathological images of penile cancer using the PCPAm dataset. insights regarding its behavior in classification. Additionally, we compared our approach with other studies, highlighting its competitive performance, which surpassed some other works in image classification. However, some limitations, such as the small dataset size, still need to be addressed in future research. An increase in dataset size could significantly enhance model performance. Nevertheless, obtaining patient samples and producing additional images presents substantial challenges, requiring collaboration with healthcare researchers, hospitals, and ethics committees. In future work, we will investigate automated optimization methods to identify the best combination and configuration of neural network blocks for specific tasks. This would include exploring different depths and variations of the blocks used, opening the possibility of adopting other types of components to build a specialized neural network architecture. In addition, new image preprocessing techniques can be explored to improve image quality before model training. This is particularly relevant in medical contexts, where image quality can vary significantly. We also plan to explore new labels in the PCPAm dataset, such as tumor grade and the presence of HPV. Data curation, R.M.G.d.C., A.M.T., L.L.d.S., H.O.B. and F.C.B.V. ; Conceptualization, M.G.M.L., G.B.J. and J.D.S.d.A. ; methodology M.G.M.L., J.G.A.d.V., J.R.D.R.J., G.B.J. and J.D.S.d.A. ; validation, A.C.S., A.C., J.D.S.d.A. and R.M.G.d.C. ; formal analysis, A.C.S., R.M.G.d.C. and G.B.J. ; investigation, M.G.M.L. and G.B.J. ; writing--original draft preparation, M.G.M.L., G.B.J. and J.D.S.d.A. ; writing--review and editing, M.G.M.L., G.B.J., J.G.A.d.V. and J.R.D.R.J. ; supervision, G.B.J. ; project administration, G.B.J. All authors have read and agreed to the published version of the manuscript. This work was supported by Fundacao para a Ciencia e Tecnologia, IP (FCT) within the R&D Units Project Scope: UIDB/00319/2020 (ALGORITM); the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)--Finance Code 001 and CAPES PDPG Amazonia Legal 0810/2020--88881.510244/2020-01; the Fundacao de Amparo a Pesquisa e ao Desenvolvimento Cientifico e Tecnologico do Maranhao (FAPEMA); and the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq). Not applicable.