## 4 Results and Discussion  
The results analyzed based on the F1-Score, shown in Tables 3 and 4, highlighted the performance in classifying histopathological images of penile cancer. Performance metrics such as precision, recall, accuracy, and F1-score report promising results across various architectures. Still, particularly the best results were observed in the I + D + M + M architecture for 40\(\times\) magnification (experiment 6), which achieved an accuracy of 94.95%, a recall of 98.18%, a precision of 94.05%, and an F1-score of 95.78%. In this case, we can verify that the insertion of MBConv blocks in stages 2 and 3 can yield good results where more layers capture local features. Adding MBConv blocks in stage 3 after a DenseNet block also produced interesting results (experiment 1). Furthermore, adding Transformer blocks in stages 2 and 3 (experiment 3) resulted in a lower F1-Score than other architectures in the conducted experiments, as did adding them only in stage 2 (experiment 5). This suggests that Transformers may be more effectively utilized in the final stage, where they can capture more global features, following stages that include MBConv or DenseNet blocks, as seen in experiments 2 and 4. For 100\(\times\) magnification, the I + D + D + M architecture (experiment 1) achieved an accuracy of 93.79%, recall of 100.00%, precision of 90.71%, and F1-score of 94.99%. In this case, the use of MBConv blocks in the final stage, after the DenseNet block, can lead the model to good performance, as well as in stage 2 before the Transformer blocks (experiment 4). Additionally, the results indicate that the F1-Score tends to decrease with the use of Transformers in the final stage when preceded by another DenseNet stage (experiment 2) or other Transformer blocks (experiment 3). Similarly, this metric was low when using MBConv in the final stage if it follows another stage that contains Transformer blocks (experiment 5) or MBConv blocks (experiment 6). Furthermore, changing the image magnification substantially affects the performance of some architectures. In contrast, others, such as I + D + D + M and I + D + M + T, present a robust F1-Score at both magnifications (40\(\times\) and 100\(\times\)), providing multi-scale solutions. The F1-Score values in these configurations show only a small difference, suggesting that they maintain consistent performance regardless of the magnification. This consistency is attributed to the effective combination of MBConv and Transformer block types in DenseNet, which allows for extracting relevant features at different scales. Additionally, Table 5 compares the proposed approach with other related works that use the same dataset for evaluation. We highlight promising results in this study, which surpass some works [12; 13]. One of these works uses a soft attention layer at the end of DenseNet-121 [12], instead of replacing blocks in the network, as we have performed in some experiments. At the same time, another employs DenseNet-201 with an attention mechanism on the skip connections [13]. However, our results indicate that replacing DenseNet blocks with other types may be a viable strategy for improving model outcomes.