COVID-19 ResNet: Red neural residual para la clasificación de la COVID-19 con aumento de imágenes con optimización bayesiana

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Maria Baldeon calisto
Javier Sebastián Balseca Zurita
Martin Alejandro Cruz Patiño

Resumen

La COVID-19 es una enfermedad infecciosa causada por un nuevo coronavirus llamado SARS-CoV-2. El primer caso apareció en diciembre del 2019 y hasta el momento sigue representando un gran desafío a nivel mundial.  La precisa detección del virus en pacientes COVID-19 positivos es un paso crucial para reducir la propagación de esta enfermedad altamente contagiosa.  En este trabajo se implemente una red neuronal residual convolucional (ResNet) para el diagnostico automatizado de la COVID-19. La ResNet implementada puede clasificar la radiografía del tórax de un paciente en COVID-19 positivo, neumonía causada por otro virus o bacteria, y paciente saludable. Además, para aumentar la precisión del modelo y superar la escasez de imágenes médicas en el set de entrenamiento, se aplica una estrategia de aumento de datos personalizada utilizando la optimización bayesiana en tres pasos. La ResNet propuesta alcanza un 94% de precisión, 95% de sensibilidad y 95% en el F1-score en set de preba. Adicionalmente, presentamos las operaciones de aumento de datos que ayudaron a incrementar el rendimiento de la red neuronal y que pueden ser utilizados por otros investigadores en el desarrollo de modelos para la clasificación de imágenes médicas.

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Baldeon calisto, M., Balseca Zurita, J. S., & Cruz Patiño, M. A. (2021). COVID-19 ResNet: Red neural residual para la clasificación de la COVID-19 con aumento de imágenes con optimización bayesiana. ACI Avances En Ciencias E Ingenierías, 13(2), 19. https://doi.org/10.18272/aci.v13i2.2288
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SECCIÓN C: INGENIERÍAS

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