## 4 Results
### Experimental Results
#### 4.3.2 Qualitative Evaluation  
The segmentation results accurately depict the edges of the liver and tumor regions, which is crucial for defining the shape and size of these structures. The segmented areas closely align with the ground truth images, ensuring that the boundaries of the liver and tumor are well defined. This precise outlining indicates that the segmentation process was performed accurately, with no significant deviations from anatomical features. In Figure 9, the liver is shown in red, while the tumor is highlighted in yellow. Both regions are detected smoothly, without abrupt changes, demonstrating the effectiveness of the proposed system in identifying and segmenting the liver and tumor. \begin{table}
\begin{tabular}{c c c} \hline
**Method** & **SSIM** & **PSNR** \\ \hline Spatial CNN & 0.93 & 36.2 \\ SWT-CNN & 0.94 & 37.1 \\ Combined & 0.96 & 38.2 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance of image enhancements. Figure 9: Segmentation output of proposed model for 3 DIRCADb dataset. In Figure 10, a comparison is presented between the input images and their corresponding segmentation results. The liver is visually represented in purple, while the tumor is highlighted in pink. The segmentation results demonstrate the algorithm's effectiveness in accurately identifying and distinguishing between the liver and tumor regions within the CT images. This visual analysis underscores the robustness of the proposed method in handling complex anatomical structures. Performance metrics for the liver and tumor segmentation of Patch-Attn-UNet, Tiles-Attn-UNet, and the proposed method are presented in Table 2. Patch-Attn-UNet achieves liver segmentation accuracies of 95.4%, with sensitivity and specificity at 95.6% each. The accuracy values for tumor segmentation are 95.1%, and sensitivity and specificity are 88.4 and 90.1, respectively. The Tiles-Attn-UNet does better on all metrics than Patch-Attn-UNet, achieving liver and tumor segmentation accuracies of 96.1% and 95.8%, respectively, while maintaining both higher sensitivity and specificity. With the highest liver and tumor segmentation accuracies of 98.5% and 97.2%, respectively, the proposed method outperforms both in terms of segmentation performance. Figure 11 illustrates the training plot, showing a rapid increase in accuracy that reaches a peak within fewer iterations. The simultaneous decrease in loss indicates that the network efficiently learns and extracts relevant features from the data. The proposed model outperforms existing segmentation methods, including UNet, ResUNet, UNetAttn, Patch Attention, and Tile Attention. By applying the attention mechanism across multiple scales, the model effectively prioritizes important regions for segmentation. The qualitative results demonstrate that models utilizing dual-attention mechanisms \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Liver**} & \multicolumn{3}{c}{**Tumor**} \\ \cline{2-7}  & **Accuracy** & **Sensitivity** & **Specificity** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Patch-Attn-UNet & 95.4 & 95.6 & 95.6 & 95.1 & 88.4 & 90.1 \\ Tiles-Attn-UNet & 96.1 & 95.2 & 96.4 & 95.8 & 89.7 & 90.1 \\ Proposed & 98.5 & 96.4 & 97.5 & 97.2 & 91.1 & 92.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of integrated models on the 3 DIRCADb dataset. Figure 10: Visual segmentation results of proposed model. can achieve superior performance in liver tumor segmentation. Figure 12 shows the qualitative results from the data. As shown in Figure 13, the UNet, ResUNet, and UNetAttn models demonstrate varying degrees of accuracy in segmenting liver tumors. However, notable discrepancies are observed, particularly in the outer tumor regions, where segmentation errors occur. Specifically, portions of the tumor are incorrectly classified as background (red), leading to incomplete tumor representation.