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Since 2019, the global dissemination of the Coronavirus and its novel strainshas resulted in a surge of new infections. The use of X-ray and computedtomography (CT) imaging techniques is critical in diagnosing and managingCOVID-19. Incorporating artificial intelligence (AI) into the field of medicalimaging is a powerful combination that can provide valuable support tohealthcare professionals.This paper focuses on the methodological approach ofusing machine learning (ML) to enhance medical imaging for COVID-19diagnosis.For example, deep learning can accurately distinguish lesions fromother parts of the lung without human intervention in a matter ofminutes.Moreover, ML can enhance performance efficiency by assistingradiologists in making more precise clinical decisions, such as detecting anddistinguishing Covid-19 from different respiratory infections and segmentinginfections in CT and X-ray images, even when the lesions have varying sizes andshapes.This article critically assesses machine learning methodologies utilizedfor the segmentation, classification, and detection of Covid-19 within CT andX-ray images, which are commonly employed tools in clinical and hospitalsettings to represent the lung in various aspects and extensive detail.There isa widespread expectation that this technology will continue to hold a centralposition within the healthcare sector, driving further progress in themanagement of the pandemic.

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