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Related Concept Videos

Glaucoma: Overview01:25

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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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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, 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: Jan 11, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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DB-SegNet: optimized framework for glaucoma detection and optic structure segmentation from retinal fundus images.

J Prakash1, B Vinoth Kumar2

  • 1Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamilnadu, India. jpk.cse@psgtech.ac.in.

Scientific Reports
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning framework, DB-SegNet, accurately segments optic discs and cups in retinal images for early glaucoma detection. It overcomes common challenges, showing high accuracy in clinical trials.

Keywords:
ClassificationGlaucomaOptic cupOptic discOptimizationSegNetSegmentationTransformer

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a leading cause of irreversible blindness due to optic nerve damage, often diagnosed late.
  • Accurate segmentation of optic disc and cup in retinal images is crucial for calculating the cup-to-disc ratio, a key glaucoma biomarker.
  • Current deep learning models face generalization issues due to image quality variations, occlusions, and ambiguities, limiting diagnostic accuracy.

Purpose of the Study:

  • To introduce DB-SegNet, an advanced diagnostic framework to improve optic disc and optic cup segmentation accuracy and enhance glaucoma detection.
  • To address limitations in existing deep learning approaches for retinal image analysis in glaucoma diagnosis.

Main Methods:

  • The DB-SegNet architecture integrates a Dilated Atrous Context Module (DACM) for multi-scale features and a Bidirectional Feature Calibration Unit (BFCU) for boundary refinement.
  • Feature space optimization utilized the Bitterling Fish Optimization (BFO) algorithm.
  • A Multi-Scale Attention Transformer (MSAT) was employed for long-range dependencies, with Honey Badger Optimization (HBO) for hyperparameter tuning.

Main Results:

  • DB-SegNet achieved high segmentation performance with Dice coefficients of 99.2% for optic disc and 98.3% for optic cup.
  • Classification accuracies reached 98.7% on the RIM-ONE dataset and 99.1% on the ORIGA-Light dataset.
  • The framework demonstrated robust performance across Drishti-GS1, RIM-ONE, and ORIGA-Light benchmark datasets.

Conclusions:

  • DB-SegNet effectively overcomes limitations of current deep learning techniques for glaucoma diagnosis.
  • The proposed framework shows significant potential as a clinically reliable tool for large-scale glaucoma screening and early intervention.
  • High accuracy in segmentation and classification supports the clinical utility of DB-SegNet for glaucoma management.