# Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model
## 5 Experimental Results
### Experiments  
After completing the preprocessing of the image data, the 131 image samples were divided into a training set and a validation set in an 8:2 ratio. In the training experiments, a crucial model in Keras, called Keras.callbacks, was used to prevent overfitting. The monitored value selected was \"val loss\" and set to minimize. If the monitored value did not continue to decrease, the training was stopped after 20 epochs without improvement. The learning rate was reduced when the evaluation metric no longer improved, with the number of epochs without improvement set to 20 and the learning rate decay rate set to 0.1. The total number of epochs was set to 100, with a batch size of 4. The Adam optimizer was used, and the learning rate followed the Stochastic Gradient Descent (SGD) algorithm. The model is based on the U-Net architecture and incorporates residual convolutional modules and attention modules. To further validate the performance of our proposed method, experiments were conducted on the same dataset and compared with the current state-of-the-art methods for liver tumor segmentation. We obtained the segmentation results shown in Table 2 for different network configurations. Figure 5: Geometrically transformed liver images and corresponding label images. (**a**) Liver images; (**b**) labelled images. From Table 2, it can be observed that the model showed an overall improvement when the attention modules and multi-scale convolution modules were added, with liver tumor segmentation achieved with a Dice score of over 0.69. Among them, the addition of attention modules alone showed limited improvement, but the highest Dice score was achieved when both the attention and multi-scale convolution modules were added, with a 9.5% improvement compared to the original U-Net. The results of different attention module variants are shown in Table 3, indicating a significant improvement in the model's performance compared to the baseline U-Net after incorporating the proposed modules. The comparison of the liver tumor segmentation results between the U-Net model and our proposed algorithm is shown in Figure 6. The displayed results were obtained from the same test case data. Both models accurately identified the target for segmentation, but our algorithm achieved better segmentation results by accurately covering a larger area of the true segmentation. From Figure 7, it can be seen that the proposed method achieves segmentation results closer to the ground truth labels compared to other U-Net variants (the last group of data in the figure show that our method has a dice value of 0.925, while the Hy-CompNet and MS-DG methods have dice values of 0.904 and 0.881, respectively). The experimental results demonstrated that our newly proposed attention module effectively captured the spatial information and addressed the semantic gap problem by enhancing the meaningful features and suppressing the irrelevant features. Our algorithm outperformed other models on the LiTS dataset. This study primarily focused on addressing issues such as the omission of small objects and unclear boundaries in liver CT images. We proposed an improved liver tumor segmentation method based on the modified U-Net architecture. We designed a novel attention mechanism to enhance the importance of relevant features in the U-shaped neural network. By utilizing residual connections, we reduced the number of parameters in the model and overcame the \"vanishing gradient\" phenomenon. We evaluated our proposed liver tumor segmentation method using the LiTS dataset to demonstrate its effectiveness. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Dice** & **Recall** & **Precision** \\ U-Net & 0.632 & 0.576 & 0.844 \\ Hy-CompNet [29] & 0.657 & 0.611 & 0.838 \\ MS-DG [30] & 0.681 & 0.631 & 0.822 \\ RDCTrans U-Net [22] & 0.668 & 0.628 & 0.832 \\ FSF-U-Net [20] & 0.684 & **0.636** & 0.833 \\
**Ours** & **0.692** & 0.635 & **0.847** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with published mainstream methods.