Glaucoma Detection From Retinal Fundus Images

The paper provides a convolutional neural network (CNN) based framework for glaucoma identification using fundus pictures of the eye. The proposed method correctly classifies fundus pictures as solid or glaucomatous, offering a simple and efficient way to detect early indications of glaucoma. Glaucoma is a condition that impairs normal eye vision. It is believed that this illness results in permanent vision loss. To date, several deep learning (DL) models have been developed for precise glaucoma localization. Because of the tremendous progress enabled by the use of convolutional neural networks (CNN), this study suggests an idea for the accurate diagnosis of glaucoma. CNN may be used to distinguish between sample sets with and without glaucoma. For segmentation, CNN offers a differentiable, leveled image architecture. The suggested work may be assessed on a total of six levels. Here, the dropout mechanism is also used to produce a nice presentation at the glaucoma site.