La Contribución de RESNET34 en la detección de COVID-19, utilizando radiografías de tórax
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En la lucha contra el avance del COVID-19, las imágenes de radiografías de tórax se convierten en una alternativa en la detección del virus. El interpretar las placas radiográficas para la detección del virus en ocasiones se complica por la presencia de otras enfermedades respiratorias. Se propone como complemento en la detección del virus, una red neuronal convolucional con arquitectura RESNET34 que permiten clasificar las imágenes de radiografías de tórax con COVID-19 e imágenes NOCOVID. El modelo presenta un grado de confiabilidad del 94,78% en la predicción de las imágenes en las dos clases COVID-19 y NOCOVID y un error en la predicción de las imágenes del 5,22%. Los resultados obtenidos, muestran un alto grado de predicción y clasificación de casos de COVID por encima del 90%, demostrando el aporte de la inteligencia artificial en el desarrollo de tecnologías preventivas de enfermedades respiratorias, además de apoyo para el personal médico.
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