Abstract
A new biomedical image segmentation method based on deep learning networks has been constructed. Under the framework of deep learning, a dual-branch deep learning network structure was designed to perform average pooling and maximum pooling on input image data, respectively. The results of pooling processing are sent to multi-layer convolution for further processing, where 2D convolution and dilated convolution are used, respectively, and the more flexible SoftMax function is selected for activation processing. Multiple fully connected processes were used between each link and between two branches to form better fusion criteria. The experimental results show that our method has a more ideal segmentation effect in both homologous and heterogeneous training segmentation experiments, with AC, SE, and SP indicators reaching over 95%, 84%, and 99%, respectively.
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