An Analysis Review, Detection Coronavirus Disease 2019 (COVID-19) based on Biosensor Application

Bakr Taha; Norhana Arsad; Yousif Al Mashhadany

doi:10.20944/preprints202008.0597.v1

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preprints.org > engineering > biomedical & chemical engineering > doi: 10.20944/preprints202008.0597.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

An Analysis Review, Detection Coronavirus Disease 2019 (COVID-19) based on Biosensor Application

Bakr Taha ^* ,

Norhana Arsad ^* ,

Yousif Al Mashhadany ^*

Version 1 : Received: 25 August 2020 / Approved: 27 August 2020 / Online: 27 August 2020 (08:01:55 CEST)

A peer-reviewed article of this Preprint also exists.

Journal reference: Sensors 2020, 20, 29
DOI: 10.3390/s20236764

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Abstract

The global spread of coronavirus disease (COVID -19) worldwide has had a significant effect on social and economic growth. The contamination keeps on advancing quickly and eccentrically, representing a significant test to its recognition and conclusion. Coronaviruses are commonly recognized by seclusion from tests, regardless of whether natural or clinical, utilizing some atomic science procedures, which can take a few days. In this work an analytical review of virus transmission, methods of diagnosing COVID -19 using artificial intelligence techniques to classify images and types of biosensors. At long last, the deformities and points of interest of each kind of sensor are recognized and examined. This exploration gives an explanatory audit of the utilization of crown infection COVID-19 in 2019. Related examinations were led utilizing five dependable databases, for example, Science Direct, IEEE Xplore, Scopus, Web of Science, and PubMed. An acceptable investigation is remembered for this audit, which can be depended upon as a logical database to put resources into another technique for recognizing COIVD-19.

Subject Areas

COVID-19 detection; biosensor application; COVID-19 transmission styles; sensors interaction; artificial intelligence

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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