Abstract
Super-resolution reconstruction helps doctors clearly observe the details of medical lesion images and increases the likelihood of the disease being diagnosed and cured. In this paper, we propose an efficient medical lesion image super-resolution method based on deep residual networks. First, a multi-scale super-resolution reconstruction model based on a deep residual network is trained. Second, an easy-to-use interface is designed. Third, a multi-scale super-resolution reconstruction model is used to reconstruct different types of medical lesion images with different scales and calculate their peak signal-to-noise ratios and structural similarity index values. The experimental results show that the proposed super-resolution reconstruction method achieves superior performance over the other methods compared in this work.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
