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Identificación de múltiples enfermedades oculares utilizando una red neuronal convolucional cuántica híbrida con
Ans Ibrahim Mahameed Alqassab1,2, M-Á Luque-Nieto3,4, Mazin Abed Mohammed5,6
1Telecommunications Engineering School, University of Malaga, Málaga, 29010, Spain.
Scientific reports
|January 31, 2026
Resumen
Un nuevo modelo híbrido de red neuronal convolucional cuántica (QCNN) diagnostica con precisión siete enfermedades oculares comunes a partir de imágenes de fondo de ojo. Este avance ayuda en la detección y el tratamiento tempranos de afecciones oculares que causan discapacidad visual.
Área de la Ciencia:
- Oftalmología e Imagen Médica
- Computación Cuántica en la Atención Médica
- Inteligencia Artificial en Diagnóstico
Sus antecedentes:
- La discapacidad visual global exige herramientas de diagnóstico avanzadas para enfermedades oculares.
- La identificación precisa y temprana de afecciones como el glaucoma y la retinopatía diabética es fundamental.
Objetivo del estudio:
- Desarrollar un modelo de diagnóstico novedoso para identificar siete enfermedades oculares comunes utilizando imágenes clínicas de fondo de ojo.
- Mejorar las capacidades de extracción y clasificación de características para mejorar la precisión diagnóstica.
Principales métodos:
- Aplicación de filtrado de difusión anisotrópica y transformada de wavelets para la mejora de imágenes.
- Implementación de técnicas de aumento específicas para abordar el desequilibrio de datos.
- Desarrollo de una red neuronal convolucional cuántica híbrida (QCNN) que integra la agrupación convolucional cuántica con CNN clásicas.
Principales resultados:
- El modelo QCNN logró una precisión de clasificación del 94% en el conjunto de datos OIA-ODIR.
- Demostró un rendimiento superior en comparación con puntos de referencia establecidos como Fundus-DeepNet y ResNet-101.
- Mostró mejoras significativas en precisión, recuperación y puntuación F1 para la clasificación de enfermedades oculares.
Conclusiones:
- El modelo híbrido QCNN propuesto es eficaz para el diagnóstico ocular multienfermedad temprano.
- Esta tecnología tiene un potencial significativo para apoyar la toma de decisiones clínicas en oftalmología.
- La integración avanzada de la IA y la computación cuántica puede revolucionar los diagnósticos oftálmicos.
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