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Glaucoma: Overview

Glaucoma: Overview

Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...

Open Angle Glaucoma: Treatment

Open Angle Glaucoma: Treatment

In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
Drugs such as carbonic anhydrase inhibitors, α2- and...

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Related Experiment Video

Updated: May 25, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

A Robust Approach to Early Glaucoma Identification from Retinal Fundus Images using Dirichlet-based Weighted Average

Mohamed Mouhafid¹, Yatong Zhou¹, Chunyan Shan²

¹School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China

Current Medical Imaging

|February 28, 2025

Summary

This summary is machine-generated.

This study introduces an ensemble deep learning model for accurate glaucoma detection from retinal images. The automated approach enhances diagnostic performance, offering a scalable solution for early visual impairment prevention.

Keywords:

CNN.Ensemble Learning. transfer learning Glaucoma Detection Image Classification Bayesian Optimization

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Last Updated: May 25, 2025

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Area of Science:

Ophthalmology
Computer Science
Artificial Intelligence

Background:

Glaucoma is a leading cause of irreversible blindness worldwide.
Manual diagnosis of retinal fundus images (RFIs) for glaucoma detection (GD) is inefficient.
Existing automated GD methods often require manual hyperparameter tuning.

Purpose of the Study:

To develop an improved automated glaucoma detection system.
To enhance diagnostic accuracy and model generalization using ensemble learning.
To integrate deep learning models with automated hyperparameter optimization.

Main Methods:

Utilized 1,355 RFIs from ACRIMA and ORIGA datasets.
Employed an ensemble of a custom CNN, MobileNet, and DenseNet201.
Applied Bayesian Optimization for automated hyperparameter tuning.
Combined model predictions using a Dirichlet-based Weighted Average Ensemble (Dirichlet-WAE).

Main Results:

Achieved state-of-the-art performance with 95.09% accuracy, 95.51% precision, 94.55% sensitivity, 94.94% F1-score, and 0.9854 AUC.
The Dirichlet-WAE significantly reduced the false positive rate.
The ensemble model outperformed individual models across all metrics.

Conclusions:

Ensemble learning and automated optimization significantly improve glaucoma detection accuracy.
The Dirichlet-WAE is crucial for balanced and accurate diagnostic performance.
Ensemble methods are vital for robust medical diagnosis in ophthalmology.