A Prototype for Interpretation of Sars-Cov-2 Tests Using Artificial Vision
3rd International Conference on Information Systems and Software Technologies, ICI2ST 2022
; : 8-13, 2022.
Article
Dans Anglais
| Scopus | ID: covidwho-2301602
ABSTRACT
Covid-19 has been declared a pandemic by the World Health Organization in March 2020, so science has been trying to help mitigate its effects from its various fields of study. Machine learning methods can play an important role in identifying test results that reveal whether an individual has the disease. This degree work presents a prototype based on computer vision and machine learning techniques to automatically detect SARS-CoV-2 serology tests. The goal of the prototype is to identify and classify the serology test cassette result by Immunoglobulin G and Immunoglobulin M indicators that are flagged after a test reaction time which is approximately 15 minutes. The results in the identification performed by the prototype are promising and ease its analysis, reducing the errors in the identification of the test and the interpretation of the results. The result is a prototype that allows to perform, simplify and improve the tasks of health professionals, which they must perform daily in the triage area. © 2022 IEEE.
artificial vision; classes; convolutional neural network; SARSCOV-2; Serology tests; Convolutional neural networks; Coronavirus; Learning systems; Machine learning; Class; Immunoglobulin G; Machine learning methods; Machine learning techniques; Serology test; Test reactions; Vision learning; World Health Organization; Computer vision
Texte intégral:
Disponible
Collection:
Bases de données des oragnisations internationales
Base de données:
Scopus
langue:
Anglais
Revue:
3rd International Conference on Information Systems and Software Technologies, ICI2ST 2022
Année:
2022
Type de document:
Article
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