SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images Article Swipe
Bakr Ahmed Taha
,
Yousif Al Mashhadany
,
Abdulmajeed Al-Jumaily
,
Mohd Saiful Dzulkefly Zan
,
Norhana Arsad
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/v14112386
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/v14112386
The SARS-CoV-2 virus is responsible for the rapid global spread of the COVID-19 disease. As a result, it is critical to understand and collect primary data on the virus, infection epidemiology, and treatment. Despite the speed with which the virus was detected, studies of its cell biology and architecture at the ultrastructural level are still in their infancy. Therefore, we investigated and analyzed the viral morphometry of SARS-CoV-2 to extract important key points of the virus’s characteristics. Then, we proposed a prediction model to identify the real virus levels based on the optimization of a full recurrent neural network (RNN) using transmission electron microscopy (TEM) images. Consequently, identification of virus levels depends on the size of the morphometry of the area (width, height, circularity, roundness, aspect ratio, and solidity). The results of our model were an error score of training network performance 3.216 × 10−11 at 639 epoch, regression of −1.6 × 10−9, momentum gain (Mu) 1 × 10−9, and gradient value of 9.6852 × 10−8, which represent a network with a high ability to predict virus levels. The fully automated system enables virologists to take a high-accuracy approach to virus diagnosis, prevention of mutations, and life cycle and improvement of diagnostic reagents and drugs, adding a point of view to the advancement of medical virology.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/v14112386
- https://www.mdpi.com/1999-4915/14/11/2386/pdf?version=1667195245
- OA Status
- gold
- Cited By
- 26
- References
- 69
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307623972
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307623972Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/v14112386Digital Object Identifier
- Title
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SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM ImagesWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-10-28Full publication date if available
- Authors
-
Bakr Ahmed Taha, Yousif Al Mashhadany, Abdulmajeed Al-Jumaily, Mohd Saiful Dzulkefly Zan, Norhana ArsadList of authors in order
- Landing page
-
https://doi.org/10.3390/v14112386Publisher landing page
- PDF URL
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https://www.mdpi.com/1999-4915/14/11/2386/pdf?version=1667195245Direct link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1999-4915/14/11/2386/pdf?version=1667195245Direct OA link when available
- Concepts
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Virus, Artificial neural network, Computer science, Virology, Artificial intelligence, Biology, Pattern recognition (psychology)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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26Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 11, 2023: 6Per-year citation counts (last 5 years)
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69Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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