## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Batch Size** & **Val Acc (\%)** & **Val Loss** & **Test Acc (\%)** & **Test Loss** & **AUC [CI 95\%]** \\ \hline
8 & 87.7 & 0.32 & 84.58 & 0.4 & 0.86 [0.882–0.958] \\
16 & 88.54 & 0.39 & 90.62 & 0.26 & 0.92 [0.911–0.977] \\
**32** & **94.58** & **0.18** & **93.33** & **0.21** & **0.94 [0.919–0.981]** \\
64 & 84.76 & 0.4 & 82.81 & 0.54 & 0.87 [0.827–0.921] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of various batch sizes. Table 3 gathers all the robust metrics values produced by the exploration process. Our concluded structure achieved promising results and outperformed the rest of the structures. Figure 3: Convolutional neural network architecture for CAD classification problem comparing various values for all parameters: (**a**) Testing accuracy for different image sizes; (**b**) AUC for different image sizes; (**c**) testing accuracy for different convolutional layers; (**d**) AUC for different convolutional layers; (**e**) testing accuracy for different dense layers; and (**f**) AUC for different dense layers. Following the exploration and the definition of the best CNN model, a robust technique was applied to evaluate the CNN's capabilities further. This technique is k-fold cross-validation, where k is the number of parts into which the dataset is divided. In our case, we distributed our dataset into 10 parts, of which 9 were utilized as training and 1 as testing. This method was iterated several times until each part was used for testing [41]. Table 4 presents the outcomes demonstrating that the RGB-CNN model delivers excellent robustness and efficiency. It is observed that the results produced from the 10-fold cross-validation are similar to the results produced by the data split method, indicating that the proposed model provided sufficiency and robustness (see Table 5). To cope with explainability, Grad-CAM was utilized in this research work to provide predictions with interpretability. To verify the feasibility of the Grad-CAM, we conducted experiments on the proposed medical image classification model producing improved visualization results. In addition, the results were assessed by a nuclear medicine physician to support the decisions in this domain. Figure 4 shows the visualization results produced by the Grad-CAM for each CAD category. Among them, we can distinguish the original images for each category, the heatmaps generated by the Grad-CAM method, and the visualization result generated by superimposing the original image on the heatmap. Different colors indicate the importance \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Runs** & **Accuracy (\%)** & **Loss** & **AUC** & **CI (95\%)** & **Sens** & **Spec** \\ \hline Run 1 & 92.3 & 0.17 & 0.92 & [0.938–0.986] & 1 & 0.94 \\ Run 2 & 92.3 & 0.2 & 0.93 & [0.918–0.981] & 0.98 & 0.95 \\ Run 3 & 92.3 & 0.27 & 0.94 & [0.916–0.98] & 0.97 & 0.97 \\ Run 4 & 94.23 & 0.2 & 0.93 & [0.935–0.985] & 1 & 0.97 \\ Run 5 & 94.23 & 0.15 & 0.92 & [0.940–0.987] & 1 & 0.92 \\ Run 6 & 98.07 & 0.17 & 0.93 & [0.924–0.979] & 1 & 0.91 \\ Run 7 & 92.3 & 0.18 & 0.95 & [0.922–0.978] & 1 & 0.94 \\ Run 8 & 94.23 & 0.06 & 0.96 & [0.933–0.987] & 1 & 0.97 \\ Run 9 & 94.11 & 0.16 & 0.95 & [0.949–0.993] & 1 & 0.97 \\ Run 10 & 90.3 & 0.22 & 0.91 & [0.912–0.978] & 0.87 & 1 \\
**Average** & **93.4 \(\pm\) 2.45** & **0.17** & **0.93** & **[0.929–0.983]** & **0.982** & **0.954** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Runs of 10-fold cross-validation of the proposed RGB-CNN architecture. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Image Size** & **Convolutional** & **Dense** & **Val Acc (\%)** & **Val Loss** & \begin{tabular}{c} **Test Acc** \\ **(\%)** \\ \end{tabular}