DETECCIÓN Y CLASIFICACIÓN DE LESIONES MAMARIAS EN IMÁGENES BIOMÉDICAS DE FORMA EXPLICABLE
DOI:
https://doi.org/10.56238/arev7n9-289Palabras clave:
Lesiones Mamarias, Cáncer de Mama, Imágenes Biomédicas, Aprendizaje AutomáticoResumen
Objetivo: El cáncer de mama es un importante problema de salud pública y la principal causa de muerte por cáncer en mujeres adultas en Brasil. El diagnóstico tardío contribuye a las altas tasas de mortalidad y a las cirugías invasivas. El acceso limitado a la imagenología resulta en diagnósticos en estadios avanzados, lo que refuerza la necesidad de la detección temprana para aumentar las tasas de curación y evitar procedimientos agresivos. El objetivo de este estudio es mejorar el diagnóstico del cáncer de mama, especialmente en países con prevalencia de diagnóstico tardío, mediante la aplicación de inteligencia artificial avanzada y técnicas de aprendizaje extremo a imágenes termográficas.
Métodos: El estudio utiliza inteligencia artificial (IA) avanzada y técnicas de aprendizaje extremo para analizar imágenes termográficas. El objetivo es desarrollar un sistema computacional que no solo diagnostique con precisión el cáncer de mama, sino que también proporcione explicaciones de sus decisiones de forma comprensible para los profesionales sanitarios.
Resultados: El enfoque de IA propuesto demostró eficiencia en la detección y clasificación de lesiones mamarias en imágenes termográficas. El sistema alcanzó una precisión del 89,70 % al distinguir lesiones malignas de otros diagnósticos en el mejor de los casos. Conclusión: El sistema desarrollado representa un avance significativo en el diagnóstico del cáncer de mama, especialmente en regiones donde los diagnósticos en etapas avanzadas son frecuentes. Tiene el potencial de mejorar la precisión diagnóstica, contribuir a mejores resultados para las pacientes y aumentar las perspectivas de recuperación de las pacientes con cáncer de mama.
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