Applied Study of Medical Image Segmentation Based on Convolutional Neural Network

Medical image segmentation is one of the hot topics in today's research, and researchers have found that medical image segmentation has important application prospects and potentials in the diagnosis and treatment of diseases, however, there are still some challenges and problems in the practical application. Therefore, the research topic of this paper is the research of medical image segmentation applications based on convolutional neural networks. The research methodology of this paper is to analyze the application of existing deep learning algorithms in image segmentation. It is found that the medical image segmentation method based on the convolutional neural network can effectively improve the accuracy and efficiency of segmentation results. The reliability of segmentation can be further improved by introducing medical experts' knowledge and labeled data. In summary, the research in this paper shows that the medical image segmentation method based on convolutional neural networks has the potential and advantages to improve the accuracy and efficiency of segmentation.