## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Exp.** & **Network** & **Magnification** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline
**1** & **I**\({}^{*}\) + D**\({}^{*}\) + D**\({}^{*}\) + M** & **100\(\times\)** & **93.79\% (5.18)** & **100.00\% (0.00)** & **90.71\% (6.88)** & **94.99\% (3.88)** \\
2 & **I**\({}^{*}\) + D**\({}^{*}\) + D**\({}^{*}\) + T** & 100\(\times\) & 87.68\% (5.09) & 90.91\% (9.96) & 88.19\% (4.90) & 89.09\% (5.17) \\
3 & **I**\({}^{*}\) + D**\({}^{*}\) + T** & 100\(\times\) & 89.74\% (4.54) & 94.55\% (7.27) & 88.26\% (3.57) & 91.13\% (4.19) \\
4 & **I**\({}^{*}\) + D**\({}^{*}\) + M + T** & 100\(\times\) & 93.79\% (2.16) & 98.18\% (3.64) & 91.92\% (4.88) & 94.77\% (1.56) \\
5 & **I**\({}^{*}\) + D**\({}^{*}\) + T + M** & 100\(\times\) & 89.74\% (7.19) & 92.73\% (8.91) & 89.62\% (6.29) & 90.97\% (6.56) \\
6 & **I**\({}^{*}\) + D**\({}^{*}\) + M + M** & 100\(\times\) & 89.74\% (3.09) & 94.55\% (4.45) & 88.29\% (3.56) & 91.22\% (2.76) \\ \hline \end{tabular}  
* Initial convolutions or blocks of DenseNet-201 pre-trained on ImageNet.  
\end{table} 
Table 4: Table of results with their respective standard deviations of each experiment for magnification 100\(\times\) by neural network architecture. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Exp.** & **Network** & **Magnification** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline
1 & **I**\({}^{*}\) + D**\({}^{*}\) + D**\({}^{*}\) + M** & **40\(\times\)** & 93.95\% (7.34) & 94.55\% (7.27) & 94.70\% (7.20) & 94.54\% (6.68) \\
2 & **I**\({}^{*}\) + D**\({}^{*}\) + D**\({}^{*}\) + T** & 40\(\times\) & 92.89\% (6.06) & 96.36\% (4.45) & 91.77\% (7.19) & 93.91\% (5.23) \\
3 & **I**\({}^{*}\) + D**\({}^{*}\) + T** & 40\(\times\) & 87.58\% (4.32) & 87.27\% (7.27) & 90.51\% (0.81) & 88.73\% (4.36) \\
4 & **I**\({}^{*}\) + D**\({}^{*}\) + M** & **40\(\times\)** & 93.74\% (3.95) & 98.18\% (3.64) & 91.77\% (4.89) & 94.78\% (3.27) \\
5 & **I**\({}^{*}\) + D**\({}^{*}\) + T** & **40\(\times\)** & **90.68\% (2.17) & 96.36\% (4.45) & 88.95\% (6.04) & 92.23\% (1.53) \\
**6** & **I**\({}^{*}\) + D**\({}^{*}\) + M** + M** & **40\(\times\)** & **94.95\% (5.48)** & **98.18\% (3.64)** & **94.05\% (8.38)** & **95.78\% (4.39)** \\ \hline \end{tabular}  
* Initial convolutions or blocks of DenseNet-201 pre-trained on ImageNet.  
\end{table} 
Table 3: Table of results with their respective standard deviations of each experiment for magnification 40\(\times\) by neural network architecture. We present some qualitative results in Figure 8. Analyzing image classification models with Grad-CAM, Gradient-Class Activation Mapping [42], can provide valuable insights into which regions of images the model considers most important for its decisions. By applying Grad-CAM to the models that performed best in the study--specifically, I + D + M + M for 40\(\times\) and I + D + D + M for 100\(\times\) --we observe in Figure 8 that the neural networks assigned significant importance to a considerable portion of regions within cancerous images, particularly areas containing keratin pearls and infiltrative growth. However, in all cancer samples, the tissues are entirely tumorous. For images classified as normal, the neural networks placed greater emphasis on the edges of the tissues, which are well-defined in this type of sample.