Abstract:
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-ofthe-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.
