Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10248864" target="_blank" >RIV/61989100:27240/21:10248864 - isvavai.cz</a>
Result on the web
<a href="https://www.techscience.com/cmc/v67n2/41341" target="_blank" >https://www.techscience.com/cmc/v67n2/41341</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.32604/cmc.2021.014767" target="_blank" >10.32604/cmc.2021.014767</a>
Alternative languages
Result language
angličtina
Original language name
Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19
Original language description
Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic's additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization algorithm (WOA) to select the most relevant patient signs. A set of experiments validated AIMDP performance. It demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic (AUC-ROC) curve, positive predictive value (PPV), negative predictive rate (NPR) and negative predictive value (NPV). AIMDP was applied to a dataset of hundreds of real data and CT images, and it was found to achieve 96% AUC for diagnosing COVID-19 and 98% for overall accuracy. The results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
CMC-Computers Materials & Continua
ISSN
1546-2218
e-ISSN
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Volume of the periodical
67
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
19
Pages from-to
2353-2371
UT code for WoS article
000616713000026
EID of the result in the Scopus database
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