A Survey on DL Based Frameworks for COVID-19 Radiological Diagnosis - Computer, Communication, and Signal Processing Access content directly
Conference Papers Year : 2022

A Survey on DL Based Frameworks for COVID-19 Radiological Diagnosis

Abstract

The ongoing Coronavirus disease (COVID-19) pandemic still necessitates emphasis on diagnosis and management of the outbreaks due to the emergence of new variants. This paper is an extensive survey on the implementation of Deep Learning (DL) models used for diagnosing COVID-19 from chest imaging, enriched with quantitative measures and regulatory aspects. The authors have searched, collated and categorised various models and techniques that reported different architectures with respect to COVID-19 diagnosis in the literature. This survey also briefs about quantifying metrics and the reported results are enumerated, also regulatory frameworks for public use of Artificial Intelligence (AI) in medical devices are comprehended.
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Wednesday, January 1, 2025
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Wednesday, January 1, 2025
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hal-04388142 , version 1 (11-01-2024)

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J. Kishan, G. Kalaichelvi, R. Surendiran, Bhadru Amgothu. A Survey on DL Based Frameworks for COVID-19 Radiological Diagnosis. 6th International Conference on Computer, Communication, and Signal Processing (ICCCSP), Feb 2022, Chennai, India. pp.36-45, ⟨10.1007/978-3-031-11633-9_4⟩. ⟨hal-04388142⟩
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