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Related Concept Videos

Glaucoma: Overview01:25

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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Open Angle Glaucoma: Treatment01:27

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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.
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Angle Closure Glaucoma: Treatment01:28

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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...
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An Adoptive Threshold-Based Multi-Level Deep Convolutional Neural Network for Glaucoma Eye Disease Detection and

Muhammad Aamir1, Muhammad Irfan2, Tariq Ali2

  • 1Department of Computer Science, COMSATS University Islamabad Sahiwal Campus, Sahiwal 57000, Pakistan.

Diagnostics (Basel, Switzerland)
|August 23, 2020
PubMed
Summary
This summary is machine-generated.

A new deep learning model effectively diagnoses glaucoma from retinal images, offering a faster, automated alternative to manual methods. This advanced system achieves high accuracy in detecting and classifying glaucoma stages, aiding in early intervention for this common cause of blindness.

Keywords:
ML-DCNNcomputer visionconvolutional neural networkglaucoma deep-learningglaucoma eye disease

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a leading cause of blindness resulting from retinal damage.
  • Current diagnostic procedures for glaucoma are often time-consuming and require manual expertise.
  • There is a need for automated, efficient methods for glaucoma diagnosis.

Purpose of the Study:

  • To propose and evaluate a multi-level deep convolutional neural network (ML-DCNN) for automated glaucoma diagnosis using retinal fundus images.
  • To develop a system capable of both detecting glaucoma and classifying its severity (Early, Moderate, Advanced).

Main Methods:

  • A dataset of 1338 retinal fundus images was collected and pre-processed using an adaptive histogram equalizer.
  • A two-phase ML-DCNN architecture, comprising a detection-net and a classification-net, was designed for feature extraction and classification.
  • The model's performance was evaluated using sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRE).

Main Results:

  • The proposed ML-DCNN achieved high performance metrics: SE of 97.04%, SP of 98.99%, ACC of 99.39%, and PRC of 98.2%.
  • The system demonstrated effectiveness in classifying glaucoma into Early, Moderate, and Advanced stages.
  • The results indicate the model's capability to handle complex glaucoma cases.

Conclusions:

  • The developed ML-DCNN provides an accurate and efficient automated method for glaucoma diagnosis from retinal fundus images.
  • This approach offers a competitive alternative to existing state-of-the-art systems.
  • The findings suggest significant potential for improving glaucoma screening and management through AI-driven tools.