Discrete Wavelet Transform-Based Data Fusion with ResUNet Model for Liver Tumor Segmentation

Liver tumors negatively affect vital functions such as digestion and nutrient storage, significantly reducing patients’ quality of life. Therefore, early detection and accurate treatment planning are of great importance. This study aims to support physicians by automatically identifying the type and location of tumors, enabling rapid diagnosis and treatment. The segmentation process was carried out using deep learning methods based on artificial intelligence, particularly the U-Net architecture, which is designed for biomedical imaging. U-Net was modified by adding residual blocks, resulting in a deeper architecture called ResUNet. Due to the limited availability of medical data, both normal data fusion and discrete wavelet transform (DWT) methods were applied during the data preprocessing phase. A total of 131 liver tumor images, resized to 120 × 120 pixels, were analyzed. The DWT-based fusion method achieved more successful results, with a dice coefficient of 94.45%. This study demonstrates the effectiveness of artificial intelligence-supported approaches in liver tumor segmentation and suggests that such applications will become more widely used in the medical field in the future.