Palabras clave
Redes Neuronales Convolucionales
Resonancia Magnética
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Resumen
Objetivo: Proponer un algoritmo inteligente para diagnosticar la Enfermedad de Alzheimer mediante resonancia magnética, y comparar el desempeño de cinco modelos de redes neuronales convolucionales (ResNet50, VGG19, DenseNet201, InceptionV3 y EfficientNetB7) en la clasificación automática de sus etapas. Métodos: Se realizó un estudio experimental con 416 exámenes de resonancia magnética funcional del conjunto de datos OASIS-1, con pacientes entre 18 y 96 años. Se utilizaron imágenes segmentadas entre los cortes 100 y 160, con preprocesamiento estandarizado y entrenamiento de los modelos usando el optimizador Adam (tasa de aprendizaje 10⁻³, batch size 32), con parada anticipada y reducción adaptativa del aprendizaje. Resultados: En la clasificación multiclase (Sin Demencia, Demencia Muy Leve, Leve y Moderada), ResNet50 alcanzó una precisión del 83%, DenseNet201 del 82% y VGG19 del 80%, superando a InceptionV3 (78%) y EfficientNetB7 (72%). El enfoque jerárquico binario (con y sin demencia) mostró mejoras, destacando ResNet50 con 96% de precisión. Conclusión: Las redes convolucionales muestran un desempeño prometedor para el diagnóstico asistido del Alzheimer, especialmente con estrategias jerárquicas que reducen errores al distinguir las etapas de la enfermedad.
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