Vol. 34 (2024)
Artículos de Investigación

Design of an application to detect covid-19 using convolutional neural networks and X-ray images

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  • Carlos Eduardo Belman López
Carlos Eduardo Belman López
Tecnológico Nacional de México
DOI: https://doi.org/10.15174/au.2024.3919

Publicado 2024-07-24

Cómo citar

Belman López, C. E. (2024). Design of an application to detect covid-19 using convolutional neural networks and X-ray images. Acta Universitaria, 34, 1–16. https://doi.org/10.15174/au.2024.3919

Resumen

Este documento presenta el diseño de una aplicación para detectar covid-19 utilizando redes neuronales convolucionales e imágenes de rayos X en dos escenarios (covid/No-covid y covid/Normal/Neumonía). Para evitar el sobreajuste, se utilizó aumento de datos, dropout, normalización por lotes y optimizador Adam. La red para tres clases se utilizó como modelo pre-entrenado ajustando solo la capa densa y de salida para obtener el modelo binario. Además, se realizó una optimización automatizada de hiper-parámetros como dropout, funciones de activación y número de neuronas. La tasa de aprendizaje se ajustó mediante callbacks para evadir óptimos locales. Las redes fueron convertidas al formato TensorFlow.js para integrarse en una aplicación híbrida utilizando Ionic y Capacitor, y se desplegaron mediante Firebase para brindar asistencia y soporte al generar diagnósticos. La aplicación obtuvo una exactitud del 98.61% y 96.48% para dos y tres clases, respectivamente, logrando mayor rendimiento que otras propuestas y utilizando menos parámetros de entrenamiento.

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