# RockDNet: Deep Learning Approach for Lithology Classification
## 4 Experimental Results
### CNN Models Training and Evaluation
On the other hand, Table 7 presents a complexity comparison across the employed models in terms of number of layers, depth, timing and size of each model. Comparison with other works in the literature is presented in Table 8. It can be clearly seen that the proposed method holds an advantage in comparison to similar approaches. It has a very small desk size with many fewer parameters, while maintaining a competitive accuracy. On the one hand, the proposed method outperforms the work in [2] and [32] in terms of accuracy. On the other hand, although the accuracy is slightly less compared with the work of [1], this is negligible considering the huge saving in terms of size on disk and number of parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Class** & \begin{tabular}{c} **Precision \% for** \\ **Testing Data** \\ \end{tabular} & \
\begin{tabular}{c} **Recall \% for** \\ **Testing Data** \\ \end{tabular} &
\begin{tabular}{c} **F1-Score \% for** \\ **Testing Data** \\ \end{tabular} \\ \hline \multirow{3}{*}{Darknet-53} & Carbonate & 97.8 & 100 & 98.9 \\  & Sandstone & 100 & 100 & 100 \\  & Shale & 100 & 96.6 & 98.2 \\ \hline \multirow{3}{*}{Darknet-19} & Carbonate & 95.7 & 100 & 97.8 \\  & Sandstone & 100 & 100 & 100 \\  & Shale & 100 & 93.1 & 96.4 \\ \hline \multirow{3}{*}{Resnet-50} & Carbonate & 90 & 100 & 94.7 \\  & Sandstone & 100 & 100 & 100 \\  & Shale & 100 & 82.8 & 90.6 \\ \hline \multirow{3}{*}{Proposed 15} & Carbonate & 91.8 & 100 & 95.7 \\  & Sandstone & 100 & 100 & 100 \\  & Shale & 100 & 86.2 & 92.6 \\ \hline \multirow{3}{*}{VGG 19} & Carbonate & 95.7 & 100 & 97.8 \\  & Sandstone & 100 & 100 & 100 \\  & Shale & 100 & 93.1 & 96.4 \\ \hline \multirow{3}{*}{VGG 16} & Carbonate & 93.8 & 100 & 96.8 \\  & Sandstone & 100 & 100 & 100 \\  & Shale & 100 & 89.7 & 94.5 \\ \hline \multirow{3}{*}{Alex-Net} & Carbonate & 91.8 & 100 & 95.7 \\  & Sandstone & 100 & 100 & 100 \\  & Shale & 100 & 86.2 & 92.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Precision, recall, and F1-score for each class across all employed CNN models. Figure 3: _Cont._  
Upon examining Table 8, our schemes outperform other works in terms of number of parameters and size on disk. Additionally, our work achieved a comparable classification accuracy to the work in [1, 41, 42], with exceptionally low memory footprint and parameters making it applicable in real-time systems. The closest model in [3] achieved a low accuracy of 81.33% with almost 2.5 the number of parameters used in our work. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Employed** & **Average** & **Average Recall** & **Average** & **Average** \\
**Model** & **Precision \%** & **\%** & **F1-Score \%** & **Accuracy \%** \\ \hline Darknet-53 & 99.3 & 98.9 & 99.1 & 99.22 \\ \hline Darknet-19 & 98.6 & 97.7 & 98.1 & 98.4 \\ \hline Resnet-50 & 96.7 & 94.3 & 95.5 & 96.1 \\ \hline Proposed CNN & 97.3 & 95.4 & 96.3 & 96.9 \\ \hline VGG 19 & 98.6 & 97.7 & 98.1 & 98.4 \\ \hline VGG 16 & 97.9 & 96.6 & 97.2 & 97.7 \\ \hline Alex-Net & 97.3 & 95.4 & 96.3 & 96.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average Precision, Average recall, and Average F1-score across all CNN models. Figure 3: Confusion matrices of all CNN models.