Image-based Glaucoma Classification Using Fundus Images and Deep Learning

Hiram José  Sandoval-Cuellar; Gendry Alfonso-Francia; Miguel Ángel Vázquez-Membrillo; Juan Manuel Ramos-Arreguín; Saúl Tovar Arriaga

doi:10.17488/RMIB.42.3.2

Autores/as

Hiram José Sandoval-Cuellar Universidad Autónoma de Querétaro, México
Gendry Alfonso-Francia Universidad Autónoma de Querétaro, México https://orcid.org/0000-0002-0315-1133
Miguel Ángel Vázquez-Membrillo Instituto Mexicano de Oftalmología, México
Juan Manuel Ramos-Arreguín Instituto Mexicano de Oftalmología, México
Saúl Tovar Arriaga Universidad Autónoma de Querétaro, México

DOI:

https://doi.org/10.17488/RMIB.42.3.2

Palabras clave:

Aprendizaje profundo, Diagnóstico del glaucoma, Red neuronal convolucional

Resumen

RESUMEN

El glaucoma es una enfermedad que afecta gradualmente al nervio óptico. La presión intravascular se puede controlar para prevenir la pérdida de visión, por lo que la detección temprana del glaucoma es crucial. El disco óptico ha sido un punto de referencia importante para encontrar anormalidades en la retina. El rápido desarrollo de técnicas de visión por computadora ha hecho posible el analizar las condiciones del ojo ayudando al especialista a realizar un diagnóstico utilizando una técnica no invasiva en su estadio inicial en imágenes de fondo de ojo. En este artículo, se propone una arquitectura para la detección de glaucoma utilizando aprendizaje profundo. Una red neuronal convolucional (RNC) es entrenada para extraer múltiples características, para clasificar imágenes de fondo de ojo. La exactitud, sensibilidad, y el área bajo la curva obtenidos en la base de datos ORIGA son 93.22%, 94.14% y 93.98%. El uso del algoritmo para la detección automática de la región de interés, incrementa considerablemente la exactitud de detección de glaucoma en la base de datos ORIGA.

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