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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

762
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...
762
Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

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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.
Drugs such as carbonic anhydrase inhibitors, α2- and...
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Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

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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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Related Experiment Video

Updated: Sep 12, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Dual-stage deep-learning method for glaucoma severity classification based on multiscale feature fusion.

Mohammad J M Zedan1, Siti Raihanah Abdani2, Sufian Badawi3

  • 1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia.

Experimental Eye Research
|August 4, 2025
PubMed
Summary
This summary is machine-generated.

A new Glaucoma Multiscale Feature Fusion Network (GMFF-Net) improves glaucoma classification accuracy. This advanced two-stage deep learning model effectively identifies disease severity for better patient outcomes.

Keywords:
Artificial intelligenceFeature fusionFundus imageGlaucoma screeningMultiscale features

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a chronic optic neuropathy causing visual field loss, necessitating early detection for effective treatment.
  • Current automated glaucoma classification methods struggle with accuracy due to single-stage pathways and limited feature extraction, failing to capture complex anatomical variations.
  • Scarcity of reliable datasets representing glaucoma progression stages further complicates accurate classification.

Purpose of the Study:

  • To introduce the Glaucoma Multiscale Feature Fusion Network (GMFF-Net), a novel two-stage deep learning framework for precise glaucoma severity classification.
  • To address the limitations of existing methods by incorporating multiscale feature extraction and attention mechanisms for enhanced anatomical feature capture.
  • To provide a robust solution for the accurate screening of glaucoma stages.

Main Methods:

  • Developed GMFF-Net, a two-stage framework utilizing parallel encoder heads for structural and anatomical feature extraction.
  • Integrated multiscale feature extraction and hybrid attention mechanisms within each encoder head to capture diverse receptive fields and emphasize critical regions.
  • Employed adaptive fusion modules to combine extracted feature maps before classification by a deep head in the second stage.

Main Results:

  • GMFF-Net demonstrated high efficiency in classifying glaucoma stages, outperforming seven state-of-the-art models.
  • Achieved a classification accuracy of 92.822%, precision of 0.9326, recall of 0.9174, and F1 score of 0.9296 on the Ibn Al-Haitham dataset.
  • The dual-stage framework proved effective in extracting fine-grained features crucial for accurate glaucoma assessment.

Conclusions:

  • GMFF-Net offers a superior approach to glaucoma severity classification compared to existing methods.
  • The proposed framework's ability to extract detailed features provides a promising solution for screening complex diseases.
  • This work highlights the potential of advanced deep learning architectures in improving ophthalmic diagnostics.