## 4 Discussion  
To fully analyze and discuss the details in the methodology and experimental results presented in the previous sections, we can reach the following inferences:  
1. The comparisons above (in the experimental results) show the effectiveness as well as the efficiency of TongueNet in tongue segmentation with the superiority of robustness in many complicated circumstances, which makes it a promising tool when integrated with TCTD. The proposed TongueNet achieves a better segmentation performance compared with other state-of-the-art methods on both qualitative and quantitative evaluations (refer to Figure 11 and Table 4). Furthermore, TongueNet uses less computation time compared with REF (see Table 5), indicating a much faster processing speed.
2. Although TongueNet performs better at segmentation in general, the predictions of the pixels still need improvement as it is not complete in some occasion in terms of quantitative evaluation. From Table 4, even if the mean F-score of TongueNet (95.38%) is higher than other methods, the Recall values of some samples (from the 200 test images) are lower than REF (e.g., the 11 th sample: TongueNet-97.94%, REF-99.77% Figure 14). This indicates indicating that the prediction of pixels cannot entirely cover all tongue regions. The cause of this is due to a higher loss of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **MIOU (\%)** & **MPA (\%)** & **MF-score (\%)** & **MPrecision (\%)** & **MRecall (\%)** \\ \hline Snake & 69.30 \(\pm\) 0.05 & 93.51 \(\pm\) 0.02 & 81.44 \(\pm\) 0.08 & 83.68 \(\pm\) 0.05 & 83.30 \(\pm\) 0.03 \\ Flood fill & 44.79 \(\pm\) 0.04 & 90.95 \(\pm\) 0.01 & 59.11 \(\pm\) 0.06 & 89.59 \(\pm\) 0.04 & 44.90 \(\pm\) 0.04 \\ REF & 83.10 \(\pm\) 0.06 & 96.81 \(\pm\) 0.01 & 92.13 \(\pm\) 0.06 & 88.94 \(\pm\) 0.03 & 93.45 \(\pm\) 0.04 \\ TongueNet & **93.11 \(\pm\) 0.02** & **98.45 \(\pm\) 0.01** & **95.38 \(\pm\) 0.03** & **98.72 \(\pm\) 0.02** & **94.26 \(\pm\) 0.01** \\ \hline \hline \(p\)-value & **0.002** & **0.002** & **0.010** & **0.004** & **0.015** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of TongueNet with other segmentation methods (the significance level \(\beta=0.05\))  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Computational Time (s)** \\ \hline REF & 0.302 \\ TongueNet & **0.267** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computation time comparison between TongueNet and REF.  
boundary in the tongue. Therefore, more focus will be placed at the boundary loss as part of our future work.
3. The parameters in the morphological layer determine the effectiveness of the refinement as well as the efficiency of the whole system. Since there are three filters (morphological reconstruction, open operation and close operation) (refer to Section 2.3) utilized to perform morphological processing for different purposes, the system sensitivity towards parameters is high. There exists a trade-off between the effectiveness and efficiency. To overcome the high sensitivity and trade-off, we fine-tune the system using different parameters and combinations, including the filter kernel size, type of operator, tolerance of morphological reconstruction, and the global threshold to generate the binary mask.