RaiD - A system for automated abnormality detection in chest radiographs

Abstract

Medical imaging techniques such as X-rays, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans are widely used to diagnose several medical conditions ranging from broken bones to detecting cancer tumors in the body. Traditionally, radiologists take radiographs from imaging machines and manually (using their naked eye) analyze them to detect any abnormalities based on their knowledge of what a normal radiograph without any defects looks like. This process is of course error-prone even for the most experienced radiologists especially in the case of chest radiographs. Some existing machine learning techniques categorize the images into lists of findings without proper localization of abnormalities in an input image/radiograph thus creating another challenge in the interpretation of results for radiologists. These methods have also been shown to be less accurate than their deep learning counterparts. RaiD, automates the process of abnormality detection in chest radiographs for specific classes of abnormalities through the use of a powerful deep learning algorithm called Yolo. Deep learning techniques have proven to be more accurate than traditional machine learning methods on a variety of tasks and thus justifying our need to apply them. This improved efficiency will consequently lead to a reduction in errors in diagnosis.