## 4 Results
### Results from RGB-CNN
These figures show that the best CNN configuration corresponds to batch size 8, five convolutional layers (16-32-64-128-256) and dense nodes 32-32. It emerges that dense 32-32 is the most suitable configuration concerning the dense nodes. Figure 7 shows the accuracy, loss and AUC values for various dense nodes regarding the best batch size (8) and the number of convolutional layers (16-32-64-128-256). Additionally, further exploration analysis was performed for various numbers of convolutional layers. Some indicative results are presented in Figure 8. It is observed that the model was able to increase its classification accuracy for 5 convolutional layers significantly. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pixels** &
\begin{tabular}{c} **Dense** \\ **Nodes** \\ \end{tabular} & **Val. Acc.** & **Val. Loss** & **Test Acc.** & **Test Loss** & **AUC** & **Time (s)** \\ \hline
200 \(\times\) 200 \(\times\) 3 & 32–32 & 92.73 & 0.183 & 92.15 & 0.24 & 0.885 & 748 \\  & 64–64 & 92.53 & 0.24 & 93.13 & 0.265 & 0.948 & 679 \\  & 128–128 & 91.72 & 0.23 & 89.91 & 0.22 & 0.885 & 674 \\ \hline
250 \(\times\) 250 \(\times\) 3 & 32–32 & 94.73 & 0.12 & 91.17 & 0.22 & 0.769 & 990 \\  & 64–64 & 91.21 & 0.235 & 90.36 & 0.26 & 0.873 & 971 \\  & 128–128 & 92.1 & 0.195 & 90.3 & 0.21 & 0.898 & 1089 \\ \hline
300 \(\times\) 300 \(\times\) 3 & 32–32 & 94.73 & 0.158 & 91.66 & 0.21 & 0.920 & 1547 \\  & 64–64 & 91.42 & 0.24 & 91.905 & 0.183 & 0.93 & 1387 \\  & 128–128 & 92.03 & 0.19 & 92.01 & 0.218 & 0.915 & 1409 \\ \hline
350 \(\times\) 350 \(\times\) 3 & 32–32 & 88.46 & 0.31 & 87.74 & 0.29 & 0.871 & 1854 \\  & 64–64 & 89.47 & 0.325 & 91.17 & 0.198 & 0.887 & 1856 \\  & 128–128 & 92.1 & 0.185 & 92.01 & 0.205 & 0.914 & 1910 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for various pixel sizes and dense nodes in RGB-CNN with 5 layers (16–32–64–128–256). dropout = 0.2 and batch size = 16. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pixels** & \begin{tabular}{c} **Dense** \\ **Nodes** \\ \end{tabular}  & **Val. Acc. ** & **Val. Loss** & **Test Acc. ** & **Test Loss** & **AUC** & **Time (s)** \\ \hline
200 \(\times\) 200 \(\times\) 3 & 32–32 & 87.71 & 0.323 & 92.15 & 0.31 & 0.935 & 630 \\  & 64–64 & 90.34 & 0.33 & 91.9 & 0.3 & 0.931 & 790 \\  & 128–128 & 93.82 & 0.18 & 92.04 & 0.245 & 0.923 & 707 \\ \hline
250 \(\timesTo sum-up, the best RGB-CNN architecture with this problem is: pixel size (\(250\times 250\times 3\)), batch size = 8, dropout = 0.2, conv 16-32-64-128-256, dense nodes 32.32, epochs = 500 (average run time = 1125 s). In addition, Table 4 depicts the confusion matrix of the best VGG16 architecture. Figure 9 illustrates the classification accuracies (validation and testing) with their respective loss curves for the proposed RGB-CNN architecture. Figure 10 depicts the diagnostic performance of RGB-CNN model in SPECT MPI interpretation assessed by ROC analysis for CAD patients. In the proposed method, the early stopping condition for RGB-CNN was investigated considering 100 epochs, thus providing adequate accuracy, higher than that of the other CNNs. In particular, the produced accuracy for early stopping was approximately 89% in most of the examined runs. However, using the minimum error stopping condition, the capacity of the algorithm was explored, increasing the accuracy of the RGB-CNN model up to 94% approximately. Figure 9a illustrates the precision curves presenting a smooth change in accuracy for the proposed model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**2-Classes** & **Abnormal** & **Normal** \\ \hline Abnormal & 26 & 0 \\ Normal & 1 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Best confusion matrix for the proposed RGB-CNN. Figure 8: CNN results for different numbers of convolutional layers concerning (**a**) validation and testing accuracies and (**b**) AUC values. Figure 7: Results for the best RGB-CNN configuration concerning (**a**) accuracy and AUC score and (**b**) loss.