Research On Liver Cancer Segmentation Method Based On Pcnn Image Processing And Se-resunet

This article discusses the use of convolutional neural networks (CNNs) in medical image segmentation. It elaborates on the definition of graph in graph depth learning algorithms and the basic structure and working principle of GCN. It also summarizes the progress and future challenges of graph depth learning algorithms in medical image segmentation. Additionally, it compares various advanced technologies to test the key performance indicators such as area under the precision curve and sensitivity. Furthermore, it classifies and summarizes three methods based on full convolution neural network, Unet network and its variants, as well as specific design ideas. Lastly, it summarizes the existing deep learning methods for liver segmentation and detection, and covers the improvement of different deep learning architectures for CT and MRI segmentation tasks.