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Vol. 33 Núm. 69 (2024)
Julio-Septiembre 2024: Ciencia abierta al servicio de la Ingeniería

Articulos

Clasificación explicable de imágenes dermatoscópicas para la detección de cáncer de piel tipo melanoma: un mapeo sistemático

https://doi.org/10.19053/uptc.01211129.v33.n69.2024.18060

Publicado 2024-09-30

  • Iván-Santiago Herrera-Bravo
    • ,
  • Hugo-Armando Ordoñez-Erazo
    • ,
  • Himer Avila-George

Iván-Santiago Herrera-Bravo
Universidad del Cauca image/svg+xml

Hugo-Armando Ordoñez-Erazo
Universidad del Cauca image/svg+xml

Himer Avila-George
Universidad de Guadalajara image/svg+xml

Resumen

En los últimos tiempos, los modelos de inteligencia artificial aplicados a la detección del melanoma han demostrado resultados prometedores. Sin embargo, la adopción de estas tecnologías se ha visto obstaculizada por la falta de transparencia en las decisiones automáticas. Para abordar este problema, surgió la Inteligencia Artificial Explicable (XAI), que busca reducir las brechas al proporcionar mecanismos que permiten comprender por qué un sistema toma una decisión específica. En este contexto, el presente mapeo sistemático examinó cómo se ha desarrollado la XAI en la detección del cáncer de piel tipo melanoma. Como resultado, se identifican 16 artículos científicos que aplicaron estrictamente métodos de explicabilidad a modelos de clasificación de melanoma; y se logra reconocer la incidencia del cáncer de piel tipo melanoma en Colombia.

Palabras clave

Inteligencia artificial explicable, Melanoma, Clasificación, Imágenes dermatoscopicas

PDF

Citas

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