Red Neuronal Convolucional para la detección de glaucoma en imágenes de fondo de ojo

Glaucoma is a silent disease and is the most common cause of irreversible blindness; early detection can prevent cases of blindness and improve the patient's quality of life. This research implements a convolutional neural network (CNN) for glaucoma detection in fundus images. The architecture of the proposed convolutional neural network consists of an input layer that receives an image, seven convolutional layers, five pooling layers, one flattened layer, two fully connected layers, and a two-class output layer. The proposed CNN was trained and tuned with 85 epochs using 3864 fundus images from the LAG dataset. The performance of the proposed CNN was evaluated with 990 images and 96.57% accuracy, 95.73% sensitivity and a specificity of 98.83% were obtained, which represent better performance compared to previous studies.