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An Analysis of Machine-Learning and Deep-Learning Techniques in COVID-19 Diagnosis Through Medical Images

Abstract —This COVID-19, also known as Corona virus disease, is a global epidemic that afflicted millions of people. Only by identifying the disease in the initial stages can an afflicted person be secluded. SARS-COV-2 is diagnosed using RT-PCR (Reverse transcription PCR testing), a method for analyzing and detecting viral RNA. It is, however, time demanding. This study examines the many image modalities used for detection, including as CT scans, X-rays, and ultrasound. Machine-Learning (ML) and Deep-Learning (DL) methods were proven useful and powerful tools in clinicians' arsenal. The performance indicators and insights from several data sets used by the researchers to train the model are presented. This study examines Machine Learning (ML) and Deep Learning (DL) methods for detecting COVID-19 utilizing a variety of medical imaging systems. The findings suggest that imaging properties may be crucial in detecting COVID-19. Finally, we look at the challenge of identifying COVID-19 by machine learning and deep learning algorithms, as well as possible future trends in this field of research. 

An Analysis of Machine-Learning and Deep-Learning Techniques in COVID-19 Diagnosis Through Medical Images