# Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting
## 4 Experimental Results
### Attention Mechanisms Assessment with External Imagery Sources  
Gradient-weighted class activation mapping (Grad-CAM)-based IoU allows a more comprehensive understanding of the model's performance, more specifically, by exploring \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{3}{c}{**Xception**} \\ \cline{2-3}
**Dataset Split Approaches** & **Nadam** & **SGD** \\ \cline{2-3}  & **Acc** & **Acc** \\ \hline
**SCDIS** & **0.32** & **0.27** \\ \hline
**FSCIS** & **0.25** & 0.22 \\ \hline
**Hold out** & **0.22** & 0.20 \\ \hline  & Fold 0 & 0.25 & 0.25 \\  & Fold 1 & 0.25 & 0.22 \\  & Fold 2 & 0.25 & 0.28 \\  & Fold 3 & 0.23 & 0.28 \\  & Fold 4 & 0.32 & 0.28 \\
**KFCV10** & Fold 5 & 0.23 & 0.27 \\  & Fold 6 & 0.23 & 0.23 \\  & Fold 7 & 0.25 & 0.25 \\  & Fold 8 & 0.27 & 0.23 \\  & Fold 9 & 0.25 & 0.30 \\  & Mean KFCV10 & 0.25 & 0.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of models using the \(D_{Ext\_Tst}^{GLI}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Xception**} \\ \cline{2-3}
**Dataset Split Approaches** & **Nadam** & **SGD** \\ \cline{2-3}  & **Acc** & **Acc** \\ \hline
**SCDIS** & **0.75** & 0.68 \\ \hline
**FSCIS** & **0.75** & **0.72** \\ \hline
**Hold out** & 0.72 & 0.70 \\ \hline  & Fold 0 & 0.72 & 0.73 \\  & Fold 1 & 0.72 & 0.77 \\  & Fold 2 & 0.77 & 0.70 \\  & Fold 3 & 0.73 & 0.73 \\  & Fold 4 & 0.68 & 0.75 \\
**KFCV10** & Fold 5 & 0.77 & 0.67 \\  & Fold 6 & 0.75 & 0.75 \\  & Fold 7 & 0.75 & 0.70 \\  & Fold 8 & 0.73 & 0.70 \\  & Fold 9 & 0.72 & 0.68 \\  & Mean KFCV10 & 0.73 & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of models using the \(D_{Ext\_Tst}^{BDI}\). the spatial accuracy and localization precision of the predictions. A more detailed examination of the salient features and attention areas associated with the models can be attained, aligning with eXplainable artificial intelligence (XAI) strategies, which promote enhanced interpretability and may provide directions for improving accuracies. In this section, the models that reached the highest performances in the previous assessment addressing classification tasks were considered. Before applying them in the proposed analysis, a couple of preliminary steps were carried out: (i) determining the Grad-CAM-generated area and (ii) annotating the corresponding ground truth regions in the images involved in the assessment, i.e., those related to Grad-CAM and the masks associated with the samples (Figure 14). One should note that the biases involved are highly dependent on the Grad-CAM estimations provided by the previously trained models upon \(D_{Tr-Val}^{BDI}\), \(D_{Tr-Val}^{GLI}\). As for the computation of Grad-CAM for each image, only the most salient attention area is considered, i.e., the zone of the image highlighted in red. Subsequently, for each image, a bounding box is computed around that salient area, and the distance between the resulting delimitation and the corresponding ground-truth region is calculated, based on the respective centroids. Besides the displacement between the computed centroids (Dis), three other Grad-CAM-based elements were considered as key parameters: classification accuracy (Acc), IoU, and Grad-CAM size (GCS). Regarding the analysis made for the \(D_{Ext\_T\_Rt}^{GLI}\), Figure 15 showcases the Grad-CAM outcomes for the top Xception models trained with the several considered approaches: FSCIS, FCDIS, HO, and the best classifier that resulted from KCVF10. Each image is accompanied by a short text denoting the respective key parameters formerly specified. Figure 14: Visualization of the Grad-CAM evaluation approach: (**a,b**) show the saliency maps over \(D_{Ext\_T\_Rt}^{GLI}\). and \(D_{Ext\_T\_Rt}^{BDI}\), respectively. In each one, the first row corresponds to the Grad-CAM visualization, while the second row presents the ground-truth. Following a similar organization and structure, Figure 16 depicts the results in the context of the \(D_{Ext\_Tst}^{BDI}\).