Diagnóstico de tuberculosis con redes neuronales artificiales y recursos BPPC
DOI:
https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1106Palabras clave:
Tuberculosis, Redes Neuronales, Reconocimiento de Normas Patrones AutomatizadasResumen
La tuberculosis es una enfermedad grave y contagiosa que mata a millones de personas, siendo un problema de salud pública mundial. Mientras tanto, la Inteligencia Artificial en radiología ha despertado un interés creciente por parte de los investigadores y la industria. Las soluciones para auxiliar el diagnóstico ya son una realidad, pero lejanas a poblaciones vulnerables y regiones subdesarrolladas. Por lo tanto, las soluciones accesibles son fundamentales para las personas que dependen en gran medida de las acciones y servicios públicos. Por lo tanto, proponemos un método de bajo costo computacional y alta eficiencia para ayudar en el diagnóstico de la tuberculosis. Utilizamos imágenes de radiografía de tórax y construimos un modelo de red neuronal artificial con características BPPC con y sin generación de datos sintéticos. Los resultados comparables a la literatura muestran el excepcional desempeño y bajo costo, colocándola como una solución alternativa viable.
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Derechos de autor 2023 Afonso Ueslei Fonseca, Juliana Paula Felix, Gabriel Silva Vieira, Bruno Moraes Rocha, Emília Alves Nogueira, Carlos Eduardo Egito Araújo, Deborah Fernandes, Fabrizzio Soares
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