CONSTRUCCIÓN DE UN MODELO DE RED NEURONAL CONVOLUCIONAL PARA DETECCIÓN DE ENFERMEDADES PULMONARES EN IMÁGENES DE RAYOS X
DOI:
https://doi.org/10.47820/recima21.v6i10.6849Palabras clave:
Deep Learning., Redes Neuronales Convolucionales., Enfermedades Pulmonares.Resumen
En 2019, las infecciones respiratorias inferiores afectaron aproximadamente a 489 millones de personas en todo el mundo, siendo la neumonía responsable de alrededor de 4 millones de muertes anuales, lo que representa el 7% del total de fallecimientos. Esta enfermedad es una de las principales causas de mortalidad global, con mayor impacto en los países en desarrollo, especialmente entre los niños menores de cinco años y los adultos mayores de setenta años. El diagnóstico de enfermedades pulmonares utiliza con frecuencia radiografías de tórax, que muestran la gravedad a través de opacidades pulmonares y acumulación de líquido pleural. Este estudio desarrolló un modelo basado en Redes Neuronales Convolucionales (CNN) para detectar automáticamente patologías pulmonares en imágenes de rayos X. El modelo alcanzó una precisión superior al 80%, con la matriz de confusión indicando un 91,8% de acierto para los casos sin alteraciones y un 69,5% para los casos patológicos. La solución fue integrada en una aplicación web para la clasificación de imágenes radiográficas.
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