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arXiv · 2401.07020

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
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Abstract

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.

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Sayed Amir Mousavi Mobarakeh, Kamran Kazemi, Ardalan Aarabi, Habibollah Danyal. 2024-01-13. 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. https://arxiv.org/abs/2401.07020

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