Chest X-rays remains to be the most common imaging modality used to diagnose
lung diseases. However, they necessitate the interpretation of experts
(radiologists and pulmonologists), who are few. This review paper investigates
the use of deep transfer learning techniques to detect COVID-19, pneumonia, and
tuberculosis in chest X-ray (CXR) images. It provides an overview of current
state-of-the-art CXR image classification techniques and discusses the
challenges and opportunities in applying transfer learning to this domain. The
paper provides a thorough examination of recent research studies that used deep
transfer learning algorithms for COVID-19, pneumonia, and tuberculosis
detection, highlighting the advantages and disadvantages of these approaches.
Finally, the review paper discusses future research directions in the field of
deep transfer learning for CXR image classification, as well as the potential
for these techniques to aid in the diagnosis and treatment of lung diseases