Enhancing COVID-19 Diagnosis through AI-Driven Analysis of Chest CT Images

DOI: https://doie.org/10.0618/Jbse.2024553470

Aravind Jadhav , Sanjay Pujari

Keywords:

Artificial intelligence, deep learning, COVID-19 diagnosis, chest CT images, medical imaging, convolutional neural networks (CNNs).

Abstract:

The COVID-19 pandemic has necessitated innovative approaches to expedite and enhance diagnostic capabilities. Chest computed tomography (CT) imaging has emerged as a valuable tool for identifying characteristic pulmonary manifestations of COVID-19. In recent years, artificial intelligence (AI) techniques, particularly deep learning algorithms, have been increasingly employed to aid in the analysis and interpretation of chest CT images for COVID19 diagnosis. This abstract presents a focused review of the role of AI in enhancing COVID-19 diagnosis through the analysis of chest CT images. We highlight recent advancements in AIdriven methodologies, including convolutional neural networks (CNNs), for automated detection and characterization of COVID-19-related lung abnormalities. Moreover, we discuss the potential impact of AI on improving diagnostic accuracy, reducing interpretation time, and supporting healthcare professionals in decision-making. Challenges and future directions for integrating AI-driven analysis into clinical workflows are also addressed, underscoring the transformative potential of AI in combating the COVID-19 pandemic.

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