Implementación de MobileNetV2 para detección de cáncer de pulmón mediante la Inteligencia Artificial para el cómputo móvil
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jun 30, 2026
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https://doi.org/10.36677/rici.v4i2.28076
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Osmar David Arellano Magdaleno
http://orcid.org/0009-0003-5905-3836
Marco Antonio Ramos Corchado, Dr.
http://orcid.org/0000-0003-3982-6988
Vianney Muñoz Jiménez, Dra.
http://orcid.org/0000-0003-2180-6743
http://orcid.org/0009-0003-5905-3836
Marco Antonio Ramos Corchado, Dr.
http://orcid.org/0000-0003-3982-6988
Vianney Muñoz Jiménez, Dra.
http://orcid.org/0000-0003-2180-6743
Resumen
El cáncer de pulmón es uno de los principales problemas de salud a nivel mundial. Frecuentemente, esta enfermedad se diagnostica en etapas avanzadas, lo que reduce las posibilidades de un tratamiento exitoso. Entre los principales factores de riesgo se encuentra el consumo de tabaco; sin embargo, cuando se detecta de manera oportuna, el cáncer de pulmón puede ser tratado y prolongar la vida de las personas. Los métodos tradicionales de diagnóstico se basan principalmente en técnicas de imagen, como la radiografía de tórax y la tomografía computarizada (CT), las cuales requieren procesos de análisis prolongados y dependen en gran medida de la experiencia del médico. Esta dependencia limita la detección oportuna de tumores malignos en los pulmones. En este contexto, el uso de inteligencia artificial mediante algoritmos como MobileNetV2 permite implementar herramientas de apoyo para la detección temprana del cáncer de pulmón, al identificar patrones con mayor rapidez y precisión. En este estudio se obtuvo una exactitud del 91.0%, resultado comparable con el desempeño de arquitecturas más robustas reportadas en la literatura.
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ARELLANO MAGDALENO, Osmar David; RAMOS CORCHADO, Marco Antonio; MUÑOZ JIMÉNEZ, Vianney.
Implementación de MobileNetV2 para detección de cáncer de pulmón mediante la Inteligencia Artificial para el cómputo móvil.
Ideas en Ciencias de la Ingeniería, [S.l.], v. 4, n. 2, p. 77-95, jun. 2026.
ISSN 2992-7447.
Disponible en: <https://ideasencienciasingenieria.uaemex.mx/article/view/28076>. Fecha de acceso: 26 ago. 2026
doi: https://doi.org/10.36677/rici.v4i2.28076.
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Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
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[22] K. Bhatt, T. Kumar, S. Hassan, and S. Kimothi, "2D Discrete Wavelet Transformation (2D-DWT) for Nanoscale Morphological Analysis," Prabha Materials Science Letters, vol. 2, no. 2, pp. 140–153, 2023. doi: 10.33889/PMSL.2023.2.2.010.
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