## 4 Results
### Results from RGB-CNN  
In this study, a meticulous CNN exploration process regarding the deep learning architectures of RGB-CNN was accomplished. In particular, an experimental analysis was conducted, where various drop rates (between 0.1-0.9), epochs (200 to 700), number of dense nodes (like 16-16, 32-32, 64-64, 128-128, 256-256, 512-512 and 1024-1024), pixel sizes (from 200 \(\times\) 200 \(\times\) 3 up to 450 \(\times\) 450 \(\times\) 3) and batch sizes (8, 16, 32 and 64) were tested. To prevent overfitting [62] in the proposed deep learning architectures, the authors conducted an exploratory analysis for different dropouts and numbers of epochs. According to the conducted analysis results, a dropout value of 0.2 and the set of 500 epochs were adequate to produce satisfactory results for the investigated RGB-CNN architecture. Moreover, an exploration analysis involving the testing of various pixel sizes was conducted. The best pixel size of the input images was determined as regards the classification accuracy and loss. Figures 5 and 6 illustrate the produced results in terms of accuracy for the examined pixel sizes, for both CNN-based architectures. These figures foster the successful selection of the appropriate pixel size for each architecture which is 250 \(\times\) 250 \(\times\) 3 for RGB-CNN. Following this exploration process, several configurations, including dropout = 0.2 and three batch sizes (8, 16 and 32), various pixel sizes and dense nodes in RGB-CNN model consisting of 5 layers (16-32-64-128-256) were investigated. Tables 1-3 illustrate the results produced for the relevant pixel sizes for the well-performed batch sizes of 8, 16 and 32. These results helped in the selection of the most appropriate pixel size, which is 250 \(\times\) 250 \(\times\) 3. As regards the two dense blocks that are the last ones in both architectures, a rigorous exploration process was performed to determine the best configuration in terms of accuracy (validation and testing) and loss. The results are depicted in Figures 3 and 4 regarding 4 and 5 convolutional layers, respectively. Looking at the specific figures, it emerges that 64-64 is the optimum combination for the CNN model. Figure 5: RGB-CNN architecture with 4 layers, various dense nodes and batch sizes for CAD classification problem. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pixels** & **Dense Nodes** & **Val. Acc.** & **Val. Loss** & **Test Acc.** & **Test Loss** & **AUC** & **Time (s)** \\ \hline \(200\times 200\times 3\) & 32–32 & 90.12 & 0.25 & 89.7 & 0.31 & 0.916 & 890 \\  & 64–64 & 93.41 & 0.2 & 92.64 & 0.21 & 0.935 & 831 \\  & 128–128 & 89.05 & 0.36 & 89.21 & 0.33 & 0.871 & 860 \\ \hline \(250\times 250\times 3\) & 32–32 & **94.72** & **0.16** & **93.62** & **0.12** & **0.926** & 1125 \\  & 64–64 & 92.53 & 0.25 & 93.47 & 0.13 & 0.921 & 1116 \\  & 128–128 & 82.89 & 0.44 & 80.87 & 0.18 & 0.845 & 1043 \\ \hline \(300\times 300\times 3\) & 32–32 & 90.78 & 0.17 & 88.23 & 0.28 & 0.9025 & 1736 \\  & 64–64 & 85.3 & 0.36 & 86.47 & 0.29 & 0.893 & 1641 \\  & 128–128 & 86.84 & 0.45 & 73.52 & 0.69 & 0.716 & 1469 \\ \hline \(350\times 350\times 3\) & 32–32 & 78.94 & 0.51 & 70.58 & 0.61 & 0.78 & 2200 \\  & 64–64 & 80.52 & 0.47 & 68.35 & 0.62 & 0.765 & 2221 \\  & 128–128 & 74.21 & 0.57 & 65.43 & 0.71 & 0.711 & 2185 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: 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 = 8. Figure 6: RGB-CNN architecture with 5 layers, various dense nodes and batch sizes for CAD classification problem. Next, for the selected pixel size (250 \(\times\) 250 \(\times\) 3), different batch sizes (8, 16 and 32) with various configurations in dense nodes were investigated, also utilizing the two previously best-performed architectures concerning the number of convolutional layers (which are 16-32-64-128 and 16-32-64-128-256), as presented in recent research studies [56, 57, 58]. The outcomes of this exploration are presented in Figures 5 and 6.