Diagnóstico automatizado de auscultación pulmonar pediátrica usando redes neuronales profundas

Jorge Lopez Perez; Damián Taire; Claudio Delrieux

doi:10.24215/15146774e072

Autores/as

Jorge Lopez Perez Universidad Nacional del Sur, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina https://orcid.org/0000-0001-7132-676X
Damián Taire Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Hospital Zonal “Dr. Andrés R. Isola”, Argentina https://orcid.org/0000-0001-6505-1560
Claudio Delrieux Universidad Nacional del Sur, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina https://orcid.org/0000-0002-2727-8374

DOI:

https://doi.org/10.24215/15146774e072

Palabras clave:

redes neuronales profundas, sonidos respiratorios, arquitectura VGG-16, coeficientes cepstrales en frecuencia de Mel (MFCC´s), diagnóstico de patologías respiratorias

Resumen

En este trabajo se investiga la implementación de redes neuronales profundas en la clasificación de sonidos respiratorios, una tarea determinante para el diagnóstico de enfermedades pulmonares. Para esta labor, se emplea la arquitectura VGG-16, reconocida por su eficacia en la clasificación de imágenes, la cual ha sido adaptada para procesar datos de audio. Se realizaron la recopilación y preprocesamiento del conjunto de datos de sonidos respiratorios, utili-zando coeficientes cepstrales de frecuencia de Mel (MFCC´s) como entrada de la red. Los resultados obtenidos revelan un rendimiento significativo, con una precisión del 79% en la clasificación de sonidos respiratorios. Este resultado re-salta el potencial de las redes neuronales convolucionales pre entrenadas en el campo médico. Sin embargo, persisten desafíos por superar, como la necesidad de conjuntos de datos más amplios y una comprensión más profunda de los resultados para su implementación clínica efectiva.

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