## 3 Results and Discussion
### Effects of Different Numbers of Image Slices  
Variations in the number of slices in CT images can affect the accuracy and efficiency of models for soil pore recognition tasks. We experimentally compared the performances of models with different numbers of CT image slices and analyzed the differences in the results.  
BDULSTM utilizes a bidirectional architecture to leverage the forward and backward features of adjacent images. Therefore, the number of sequential images affects the theoretical results of the model. Different numbers of input sequence images were tested for the three soil samples (i.e., three, five, seven, and nine images).  
As shown in Table 1, both accuracy and F\({}_{1}\)-score improve slightly with an increase in the number of sequence images. Although the improvement is slight, the variance tends to decrease, corresponding to increased robustness. However, the number of sequence images and evaluation indicators do not exhibit linear relationships. When the number of images increased from seven to nine, the evaluation indicators no longer improved. CLSTM has a bottleneck in terms of sequence processing power, meaning excessively long sequences lead to degraded performance. The qualitative results for different numbers of images can be found in Figure 4.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Soil Type** & **Number of Images** & **Accuracy** & **Precision** & **Recall** & **F\({}_{1}\)-Score** \\ \hline Primary & 3 & \(96.51\pm 1.02\times 10^{-2}\) & \(96.54\pm 1.55\times 10^{-2}\) & \(68.02\pm 7.54\times 10^{-1}\) & \(79.48\pm 2.93\times 10^{-1}\) \\  & 5 & \(96.47\pm 1.15\times 10^{-2}\) & \(98.23\pm 9.25\times 10^{-3}\) & \(66.37\pm 8.28\times 10^{-1}\) & \(78.85\pm 3.53\times 10^{-1}\) \\  & 7 & \(96.49\pm 1.01\times 10^{-2}\) & \(96.40\pm 1.45\times 10^{-2}\) & \(67.96\pm 7.64\times 10^{-1}\) & \(79.38\pm 2.92\times 10^{-1}\) \\  & 9 & \(96.48\pm 1.07\times 10^{-2}\) & \(96.83\pm 1.31\times 10^{-2}\) & \(67.51\pm 8.03\times 10^{-1}\) & \(79.20\pm 3.17\times 10^{-1}\) \\ \hline Frozen & 3 & \(98.72\pm 3.44\times 10^{-3}\) & \(96.59\pm 8.08\times 10^{-2}\) & \(86.80\pm 7.93\times 10^{-1}\) & \(91.08\pm 1.68\times 10^{-1}\) \\  & 5 & \(98.88\pm 1.11\times 10^{-3}\) & \(93.49\pm 2.16\times 10^{-1}\) & \(92.15\pm 4.51\times 10^{-1}\) & \(92.50\pm 5.08\times 10^{-2}\) \\  & 7 & \(98.80\pm 9.14\times 10^{-4}\) & \(92.08\pm 3.08\times 10^{-1}\) & \(92.76\pm 4.05\times 10^{-1}\) & \(92.07\pm 4.25\times 10^{-2}\) \\  & 9 & \(98.87\pm 1.40\times 10^{-3}\) & \(94.08\pm 2.29\times 10^{-1}\) & \(91.48\pm 5.40\times 10^{-1}\) & \(92.40\pm 6.85\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparisons of segmentation performance (%).  
A previous study on pulmonary nodule classification using CT scans found that using additional slices could capture more comprehensive information regarding nodule characteristics, leading to a higher performance [42]. However, there may be a limit to the benefit of adding more slices, as the computational complexity of the model may increase, leading to issues such as overfitting. Increasing the number of sequence images had a positive effect on model performance, but the training time also increased and model performance stopped improving when the number of sequence images increased to nine. Therefore, groups of seven images were selected as BDULSTM sequences based on the results of this experiment. The results of our analysis provide insights into the optimal number of slices required for pore recognition and contribute to the advancement of CT image analysis.