Super Resolution in Medical Imaging

Abstract

Medical imaging is a technique and a procedure that involves imaging of the interior portion of a human body for clinical evaluation, medical intervention, and to show how certain organs or tissues are functioning (physiology). MRI scan images of any patient can degrade in quality if they are shared over the internet or in hardcopy. This paper intends to provide an application to obtain high-resolution medical images from lower resolution images. The healthcare sector requires services and data of the best possible quality and this application would help them in better diagnosis and treatment. This paper uses Deep Learning Models and Machine Learning techniques, particularly the Super Resolution Generative Adversarial Networks, to high-resolution Brain MRI images (128 x 128 dimensions) from low-resolution images (32 x 32 dimensions). A web application is developed using the Streamlit library that can also be accessed from a mobile (responsive User Interface) for the end-to-end implementation of the paper. The underlying architecture of the SRGAN model can be replicated to other use cases with suitable customization to achieve high accuracy in generation of super-resolution images. The future scope would include the implementation of a mobile application with real-time processing of generation of high-resolution images from lower resolution images.