Aplicación de redes neuronales artificiales para la detección binaria del síndrome del ojo rojo
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https://doi.org/10.47796/ing.v8i00.1400Palabras clave:
Hiperemia ocular, Aprendizaje profundo, visión computacional, clasificación binariaResumen
El síndrome del ojo rojo es uno de los motivos más frecuentes de consulta en atención primaria, y su diagnóstico temprano resulta complejo por la similitud clínica entre diversas etiologías. En este estudio se desarrolló y evaluó un enfoque de detección binaria (“ojo rojo” vs. “normal”) comparando arquitecturas CNN, modelos basados en Transformers y un modelo híbrido. Se empleó un conjunto de 2 298 imágenes reorganizadas en dos clases, entrenadas bajo condiciones homogéneas mediante transfer learning e hiperparámetros fijos. Los experimentos se ejecutaron en Python 3.10.0 con PyTorch 2.7.1+cu118, torchvision 0.22.1+cu118, timm 1.0.17, scikit-learn 1.6.1, NumPy 1.26.4, Albumentations 2.0.8 y Matplotlib 3.8.2, sobre hardware con GPU NVIDIA RTX 4060 (8 GB). Los resultados evidenciaron alto desempeño en todos los modelos (F1 > 0.92, MCC > 0.90 y AUC ≥ 0.98). El modelo híbrido alcanzó el mejor rendimiento global (AUC = 0.996, MCC = 0.925, F1 = 0.924 y exactitud = 94.20 %). La prueba de McNemar indicó que no existen diferencias estadísticamente significativas entre el modelo híbrido y el mejor modelo individual (ResNet).
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Derechos de autor 2026 Marcelino Torres Villanueva, Juan Pedro Santos Fernández
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
