Applications of Deep Learning in Medical Imaging: A Systematic Review

With the increasing number of medical images and the continuous progress of artificial intelligence technology, the traditional medical image analysis methods relying on artificial features and experts' experience have been difficult to meet the dual requirements of efficiency and accuracy in modern clinics. In recent years, deep learning has shown strong application potential in the field of medical imaging, and has gradually become an important tool for improving image processing efficiency and diagnosis level. In this paper, from the three target directions of image segmentation, image registration and image enhancement, we systematically sort out the research progress and application of deep learning in medical image analysis, discuss its practical value in clinical practice with the current research cases, summarise the main challenges faced by the current technology, and look forward to the future development direction of deep learning in medical imaging. By collating and analysing the existing research results, it can provide useful references and ideas for the future development of intelligent analysis of medical images.