Medical Image Segmentation Algorithm Based on Deep Learning and Convolutional Neural Network

In recent years, image processing technology is constantly updated, which also makes the development of medicine into a new stage, and medical image is becoming a new science with the progress of science and technology, artificial intelligence and other fields. As an important branch of medical imaging, image segmentation has become one of the most active and dynamic problems in medical research. In this paper, we propose an improved fusion rule and depth-residual structure feature extraction network to achieve accurate segmentation and non-target edge localization between multiple regions in medical images, and conduct experiments on medical image segmentation algorithm after the improved fusion rule and depth-residual structure feature extraction network, and achieve good experimental results.