Procesamiento digital de imagen del fondo del ojo humano a través de redes neuronales para prevención y detección del glaucoma.

Abstract

This project presents a digital image processing model using the YOLOv8 neural network model for the early detection and prevention of glaucoma through human eye fundus images. A comprehensive image database was developed to train and validate the model, achieving a 90% accuracy rate in identifying glaucoma cases. The results highlight the importance of robust databases and the potential of YOLOv8 in ocular disease detection. Future work includes expanding the database for further validation and exploring the application of this model in clinical settings.