Research on the Image Localization of Tissue Sections and Gene Expression Patterns Based on the U-Net

For the problems of inaccurate localization, high recognition difficulty, and the complexity of data in gene expression patterns from tissue section images, this paper introduces a gene expression pattern localization algorithm utilizing U-Net. First, a U-Net network structure is established based on the deep learning model to extract features and segment the gene expression pattern. Second, an enhanced gene expression pattern recognition approach is suggested to address the challenge that traditional techniques struggle with complex backgrounds and noise. Third, based on the data characteristics of tissue slice images, a U-Net model that considers the effects of spatial distribution and intensity variation is developed to depict gene expression patterns accurately. Finally, a gene expression pattern recognition system based on the U-Net algorithm is built in this study. Simulation analysis and experimentation prove that the proposed algorithm can significantly improve the positioning accuracy of gene expression patterns and assist biomedical research.