An Extensive Survey of Machine Learning Based Approaches on Automated Pathology Detection in Chest X-Rays

Abstract

Radiography is one of the most common and eminent medical imaging technologies in the world to date. Chest radiography is a very powerful and successful way of diagnosing thoracic diseases of humans. With the latest advancements and development in computer hardware, computer vision and especially with the publicly available large-scale datasets, machine learning based approaches on automated pathology detection in chest radiography have become increasingly popular among researchers. Our study conducts an extensive survey on existing machine learning approaches, its datasets and techniques on pathology detection in Chest X-Rays. The paper presents popular and publicly available labelled Chest X-Rays datasets with its specifications and discusses about the labellers, labelling methodologies used by them in a comprehensive discussion. Then, popular effective Image Processing techniques for Chest X-Rays images are presented. Then the paper further discusses about the current machine learning architectures used and portraits the effectiveness of Deep Convolutional Neural Networks for the purpose. Finally, the paper concludes with a discussion with gaps in current literature, unexplored areas and possible future with them in Machine Learning based automated pathology detection on Chest X-Rays.