Research on Deep Learning-Based Liver Tumor Image Segmentation Techniques

Early detection and accurate diagnosis of liver tumors are crucial for patient prognosis. In recent years, deep learning techniques have made significant advancements in the field of medical image processing, particularly in liver tumor image segmentation. This paper reviews the latest research progress on deep learning-based liver tumor image segmentation techniques, focusing on the application of various deep learning models, including Convolutional Neural Networks (CNNs), U-Net, and its variants. By comparing the performance and efficiency of different methods, the advantages and disadvantages of each model in liver tumor segmentation tasks are discussed. Additionally, this paper proposes a new data preprocessing method called Color Preprocessing (CP), which effectively improves the segmentation accuracy of processed data under the same methods. Finally, future research directions are envisioned, highlighting the importance of deep learning technologies in improving the accuracy of liver tumor image segmentation and their potential clinical applications.