Diseño e implementación de un sistema de visión artificial para identificación de pruebas serológicas para sars-cov-2 e interpretación de resultados.

Abstract

Covid-19 has been declared a pandemic by the WHO (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. Therefore, this degree work presents a prototype based on computer vision and machine learning techniques to automatically detect SARS-CoV-2 serology tests. The objective of the prototype is to identify and classify the serology test cassette result by IgG and IgM indicators that are marked after a test reaction time which is approximately 15 minutes. The results in the identification performed by the prototype are promising and facilitate its analysis, reducing the errors in the identification of the test and the interpretation of the results. The final product is a prototype that makes it possible to perform, simplify and improve the tasks of identifying the results of such tests by health professionals in the triage area.