Adopting Deep Learning for Chest X-ray Analysis: An Extensive Survey

Chest X-ray (CXR) investigations are commonly carried out at healthcare centres. These analyses may identify critical medical illnesses such pneumonia, idiopathic pulmonary fibrosis, cardiac disease, and breaking bones, and pneumothorax. Unfortunately, radiologists frequently perform these diagnoses manually, which makes it time-consuming and prone to mistakes and misinterpretation. Researchers have been investigating various algorithms and models to build machine learning that might aid radiologists in their assessment and lower the likelihood of misinterpretation in recent years due to the growing processing capacity of hardware and developments in deep learning. To increase the precision and accuracy of the assessment, researchers have developed single- and multi-level localization and segmentation approaches. This study reviews the advancements achieved in deep learning in the context of CXR.