A 2020 Guide to Deep Learning for Medical Imaging and the Healthcare Industry

In this article, we will be looking at what is medical imaging, the different applications and use-cases of medical imaging, how artificial intelligence and deep learning is aiding the healthcare industry towards early and more accurate diagnosis. We will review literature about how machine learning is being applied in different spheres of medical imaging and in the end implement a binary classifier to diagnose diabetic retinopathy. Medical imaging consists of set of processes or techniques to create visual representations of the interior parts of the body such as organs or tissues for clinical purposes to monitor health, diagnose and treat diseases and injuries. Moreover, it also helps in creating database of anatomy and physiology. Owing to the advancements in the field today medical imaging has the ability to achieve information of human body for many useful clinical applications. Different types of medical imaging technology gives different information about the area of the body to be studied or medically treated. Organisations incorporating the medical imaging devices include freestanding radiology and pathology facilities as well as clinics and hospitals. Major manufacturers of these medical imaging devices include Fujifilm, GE, Siemens Healthineers, Philips, Toshiba, Hitachi and Samsung.