## 5 Discussion
### Impact of Multi-Scale Attention  
This study evaluates the impact of different input scales--patch and tile--on the attention mechanisms within a Residual UNet architecture for liver and tumor segmentation as measured by the Dice score, a critical metric for comparing predicted and actually segmented segmentation. Patch Attention provides a Dice score of 96.5% and concentrates on the small and localized regions of the image. As a result, this approach facilitates fine-grained feature extraction, leading to a high Dice score between segmented areas and the ground truth, and provides a powerful method for capturing the intricate boundaries of liver and tumor structures. By performing on larger image regions, however, the Tile Attention mechanism achieves a slightly better Dice score of 96.8%. This procedure provides for a better segmentation quality without sacrificing a richer contextual understanding of the spatial relations in the image and balances the attributes of detailed feature extraction. The proposed mechanism, which integrates both patch and tile strategies, achieves the best Dice score of 97.03%. However, the dual-scale approach enhances segmentation performance by leveraging magnitude by exploiting strengths both in localized and broader contextual feature extraction. In liver and tumor images, the superior Dice score reports the best overlap as well as the best segmentation quality, taking into account the complex spatial relationships and intensity variations between liver and tumor. The results demonstrate that while each Patch and Tile Attention mechanism improves the Dice score independently, the proposed mechanism yields the largest resulting improvement, resulting in a better segmentation performance. Figure 13: Comparative analysis of tumor segmentation accuracy. Tables 3 and 4 present a comprehensive performance comparison of various methods for liver and tumor segmentation evaluated on the 3 DIRCADb dataset. Table 3 evaluates three methods: proposed, Patch Attention, and Tile Attention. The proposed method yields the highest accuracy, sensitivity, specificity, Dice coefficient and VOE when applied to liver segmentation, exhibiting superior performance to achieve better liver segmentation. Similarly, in Table 4, we also evaluate the same three methods for tumor segmentation, and the proposed method again obtains the highest accuracy, sensitivity, specificity, Dice coefficient, and VOE. Attention mechanisms efficiently improve tumor segmentation, as this demonstrates. The results of the proposed method outperform the Patch Attention and Tile Attention methods consistently, which suggests the possibility of attention mechanisms in improving liver CT image analysis. Combining the patch- and tile-based approaches improves the segmentation by incorporating local details and global context. On the other hand, tile-wise processing accounts for large features in the image, while patch-wise processing captures the small-scale detail. The model combines localized and global information by integrating both methods to achieve more accurate segmentation results. The fusion of this model improves the model's robustness against image content and image size variations and gives a more complete view of the data. Figure 14 shows the ROC performance and demonstrates the effectiveness of this improved segmentation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Accuracy \%** & **Sensitivity \%** & **Dice \%** & **VOE \%** & **RVD \%** & **ASSD (mm)** \\ \hline Patch Attention & 95.1 & 88.4 & 71.1 & 29.9 & 38.5 & 1.72 \\ Tile Attention & 96.8 & 89.7 & 72.9 & 26.2 & 32.5 & 1.66 \\ Proposed & 97.2 & 91.1 & 75.4 & 22.67 & 0.288 & 1.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of attention-based methods for tumor segmentation. Figure 14: Performance analysis of ROC between patch, tile, and proposed for (**a**) liver segmentation; (**b**) tumor segmentation.