Revisión Sistemática de Técnicas De Inteligencia Artificial para Detección de Cáncer Pulmonar en Imágenes Médicas
DOI. 10.54798/AHZC2711
Palabras clave:
Cáncer de pulmón, deep learning, detección temprana, inteligencia artificial, redes neuronales convolucionales.Resumen
El cáncer de pulmón es la principal causa de muerte por cáncer en el mundo, con 2.5 millones de fallecimientos y 1.8 millones de nuevos casos en 2022, estas cifras reflejan una realidad preocupante. Las técnicas de inteligencia artificial (IA), en particular el deep learning, han mostrado gran potencial para detectar el cáncer. Esta revisión sistemática analiza las técnicas de IA aplicadas en la detección y diagnóstico de cáncer de pulmón a partir de imágenes médicas, así como los datasets empleados, las métricas de rendimiento y los métodos de preprocesamiento de imágenes. El objetivo es esclarecer el panorama a los investigadores que estén interesados en el desarrollo de herramientas de detección de cáncer de pulmón mediante IA e imágenes médicas, resaltando las principales tecnologías que se están utilizando actualmente, así como sus limitaciones. Para la revisión se ha seguido la metodología proporcionada por Kitchenham & Charters. Los artículos considerados provienen de bases de datos indexadas como Scopus, Web of Science y PubMed, publicados entre 2019 y 2023. Los resultados muestran como tecnología principal a las redes neuronales convolucionales, las cuales se utilizaron en diferentes arquitecturas, algunas de ellas se combinaron con modelos de aprendizaje automático. Los datasets más usados fueron los orientados a la detección por nódulo. La revisión concluye que la integración de modelos híbridos basados en redes neuronales convolucionales son una opción prometedora para mejorar la precisión de la detección temprana del cáncer de pulmón.
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