Optimization of U-Net Model for Liver Tumor Image Segmentation Based on Channel Attention Mechanism

This study focuses on exploring the role of channel attention mechanism in the improvement of U-Net model, especially in the field of medical image segmentation of liver tumors. By integrating channel attention mechanisms, the U-Net model can more accurately identify key features in images, significantly improving the accuracy of segmentation tasks. The experimental data shows that the optimized U-Net model performs consistently during the training and validation phases, demonstrating good generalization ability and low risk of overfitting. Especially in the 30th training cycle, the loss values of the model on both the training and validation sets decreased to the lowest and tended to stabilize, further confirming the stability and reliability of the model. On the test set, the model achieved an accuracy of 86.62% and a mean intersection to union ratio (Miou) of 83.27%, demonstrating its high predictive accuracy in liver tumor image segmentation tasks. The segmentation results of the test set further confirm the accuracy and strong generalization ability of the model. In summary, this study confirms that the U-Net model with improved channel attention mechanism has significant advantages in liver tumor image segmentation tasks. It not only improves segmentation accuracy but also enhances the model's generalization ability, which is of great significance for improving the automation level of medical image analysis. Especially in the fields of precision medicine and computer-aided diagnosis, this model can provide doctors with more accurate decision support assistance.