Empowering Medical Imaging with Artificial Intelligence: A Review of
  Machine Learning Approaches for the Detection, and Segmentation of COVID-19
  Using Radiographic and Tomographic Images
Sayed Amir Mousavi Mobarakeh Kamran Kazemi Ardalan Aarabi Habibollah Danyal
Since 2019, the global dissemination of the Coronavirus and its novel strains
has resulted in a surge of new infections. The use of X-ray and computed
tomography (CT) imaging techniques is critical in diagnosing and managing
COVID-19. Incorporating artificial intelligence (AI) into the field of medical
imaging is a powerful combination that can provide valuable support to
healthcare professionals.This paper focuses on the methodological approach of
using machine learning (ML) to enhance medical imaging for COVID-19
diagnosis.For example, deep learning can accurately distinguish lesions from
other parts of the lung without human intervention in a matter of
minutes.Moreover, ML can enhance performance efficiency by assisting
radiologists in making more precise clinical decisions, such as detecting and
distinguishing Covid-19 from different respiratory infections and segmenting
infections in CT and X-ray images, even when the lesions have varying sizes and
shapes.This article critically assesses machine learning methodologies utilized
for the segmentation, classification, and detection of Covid-19 within CT and
X-ray images, which are commonly employed tools in clinical and hospital
settings to represent the lung in various aspects and extensive detail.There is
a widespread expectation that this technology will continue to hold a central
position within the healthcare sector, driving further progress in the
management of the pandemic.