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Glaucoma: Overview

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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...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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A hybrid framework for glaucoma detection through federated machine learning and deep learning models.

Abeer Aljohani¹, Rua Y Aburasain²

¹Department of Computer Science , Applied College, Taibah University, Medina, 42353, Kingdom of Saudi Arabia. aahjohani@taibahu.edu.sa.

BMC Medical Informatics and Decision Making

|May 2, 2024

Summary

This study presents a hybrid framework using machine learning (ML) and deep learning (DL) for accurate glaucoma detection from retinal images. The system achieved 95.41% accuracy, aiding early diagnosis and preventing vision loss.

Keywords:

Convolutional neural network Deep learning Feature extraction Glaucoma eye disease Image processing and classification Machine learning

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Area of Science:

Ophthalmology
Computer Science
Medical Imaging

Background:

Glaucoma is a leading cause of blindness, often progressing asymptomatically.
Timely detection is crucial to prevent irreversible vision loss.

Purpose of the Study:

To develop an advanced computerized system for accurate glaucoma detection.
To integrate Machine Learning (ML), Convolutional Neural Networks (CNNs), and image processing for enhanced diagnostic capabilities.

Main Methods:

A hybrid framework combining CNNs (ResNet50, VGG-16) and Random Forest was developed.
Retinal images were analyzed independently by models.
Post-processing rules aggregated predictions for a comprehensive glaucoma assessment.

Main Results:

The hybrid framework achieved 95.41% accuracy.
Precision reached 99.37%, recall 88.37%, and F1 score 93.52%.
These metrics demonstrate the framework's robustness in glaucoma diagnosis.

Conclusions:

The research introduces an innovative hybrid framework for glaucoma detection.
The ensemble approach using CNNs and ML models shows significant potential for early diagnosis.
This methodology offers a promising path for advancements in ophthalmic healthcare and preventing vision loss.