Deep Learning Methodologies for Diagnosis of Respiratory Disorders from Chest X-ray Images: A Comparative Study

Abstract

Chest radiography needs timely diseases diagnosis and reporting of potential findings in the images, as it is an important diagnostic imaging test in medical practice. A crucial step in radiology workflow is the fast, automated, and reliable detection of diseases created on chest radiography. To overcome this issue, an artificial intelligence-based algorithm such as deep learning (DL) are promising methods for automatic and fast diagnosis due to their excellent performance analysis of a wide range of medical images and visual information. This paper surveys the DL methods for lung disease detection from chest X-ray images. The common five attributes surveyed in the articles are data augmentation, transfer learning, types of DL algorithms, types of lung diseases and features used for detection of abnormalities, and types of lung diseases. The presented methods may prove extremely useful for people to ideate their research contributions in this area.