# An Improved V-Net Model for Thyroid Nodule Segmentation
## 4 Experimental Results and Discussion
Despite experiencing a sudden increase in validation loss and a sudden drop in validation Dice score toward later epochs of training on the DDTI dataset, the model quickly corrected this state to an acceptable level. The validation loss and Dice score results for the TN3K dataset remained at an acceptable level. Table 2 compares three V-Net-based models with the improved V-Net model based on their Dice and IoU scores, as well as training times on the DDTI dataset. The scores highlighted in bold indicate that the model in the corresponding row produced the most  
successful result in terms of the metric in that column. Upon reviewing these results, it can be noted that although it demonstrates better performance than the V-Net, fusion V-Net, and SEV-Net models, with Dice and IoU metric values of 0.8451 and 0.7627, respectively, it incurs higher resource usage because of its training time of 7286.6776 s compared to others. Fusion V-Net obtained the lowest Dice score, with a value of 0.8103, whereas V-Net reached the lowest IoU performance, with a value of 0.7261. The Dice performance of the V-Net is also very close to that of the lowest-performing fusion V-Net in terms of this metric. However, V-Net had the lowest resource consumption, with a training time of 2680.5712 s.  
Table 3 presents the Dice and IoU scores achieved by the three V-Net-based models, along with the improved V-Net model on the TN3K dataset, including their respective training times. When the performance results were evaluated, similar to those from the DDTI dataset, it was evident that the proposed model outperformed the others, with Dice and IoU metrics scoring 0.8388 and 0.7550, respectively, while also having the highest resource consumption, with a training time of 26957.3628 s. In this dataset, SEV-Net achieved the lowest performance with a Dice score of 0.8212 and an IoU score of 0.7389, whereas V-Net had the lowest resource consumption with a training time of only 9752.1226 s. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dice** & **IoU** & **Training Times (s)** \\ \hline V-Net & 0.8136 & 0.7261 & **2680.5712** \\ Fusion V-Net & 0.8103 & 0.7401 & 4828.9201 \\ SEV-Net & 0.8295 & 0.7433 & 4839.4166 \\
**Improved V-Net** & **0.8451** & **0.7627** & 7286.6776 \\
**(Proposed model)** & **0.8452** & **0.7627** & **7286.6776** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of three V-Net-based models with the improved V-Net model based on Dice score, IoU score, and training times on the DDTI dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dice** & **IoU** & **Training Times (s)** \\ \hline V-Net & 0.8235 & 0.7460 & **9752.1226** \\ Fusion V-Net & 0.8290 & 0.7497 & 18002.7762 \\ SEV-Net & 0.8212 & 0.7389 & 10514.5439 \\
**Improved V-Net** & **0.8388** & **0.7550** & 26957.3628 \\
**(Proposed model)** & **0.8452** & **0.7627** & **7286.6776** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of three V-Net-based models with the improved V-Net model based on Dice score, IoU score, and training times on the TN3K dataset. Figure 9: The graphs of the changes in training and validation losses and Dice scores by epoch for the proposed method on the DDTI and TN3K datasets. The ability of DL methods to automatically extract features from raw image data, learn complex patterns, and generalize patterns in image data has given them a critical role in medical imaging and diagnosis, particularly for thyroid nodule segmentation. Advanced methods such as U-Net and architectures that integrate multiscale features and attention mechanisms have recently gained popularity in this area. Table 4 compares the performance of DL-based studies in the literature that used the DDTI dataset for thyroid nodule segmentation with those of the improved V-Net method.