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Related Concept Videos

Glaucoma: Overview01:25

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: Treatment01:27

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...
Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

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: Jun 25, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

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

Published on: May 25, 2020

Hypertuned boosting approach with Local Binary Pattern and Pivot Distribution Count method feature extractor for

Santosh Kumar Majhi1, Sushma Soni1, Ankita Misra2

  • 1Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, Chhattisgarh, India.

Scientific Reports
|June 23, 2026
PubMed
Summary

This study presents a hybrid approach using Local Binary Pattern (LBP) and Pivot Distribution Count (PDC) for glaucoma detection from retinal images. The method achieved 97.55% accuracy, offering a robust solution for automatic glaucoma screening.

Keywords:
Fundus Images, Glaucoma, Hyperparameter tuning, Local binary pattern, Pivot Distribution Count

Related Experiment Videos

Last Updated: Jun 25, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

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

Published on: May 25, 2020

Area of Science:

  • Ophthalmology
  • Computer Science
  • Medical Imaging

Background:

  • Glaucoma is a leading cause of irreversible blindness globally, characterized by optic nerve damage.
  • Early diagnosis is critical to prevent vision deterioration, but manual detection from fundus images is challenging.
  • Automated computer-aided detection systems are essential for accurate and efficient glaucoma screening.

Purpose of the Study:

  • To develop and evaluate a hybrid feature extraction method for improved glaucoma detection.
  • To compare the performance of various machine learning classifiers for classifying glaucoma from retinal fundus images.
  • To optimize classifier performance using advanced search and optimization techniques.

Main Methods:

  • A hybrid approach combining Local Binary Pattern (LBP) for texture analysis and Pivot Distribution Count (PDC) for white-pixel intensity and fractal dimension extraction.
  • Feature extraction from retinal fundus images.
  • Classification using Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Adaboost, Gradient Boosting, XGBoost, Light Gradient Boosting Machine (LightGBM), and CatBoost.
  • Optimization of LightGBM and CatBoost using grid search, randomized search, genetic algorithm, and Bayesian optimization.

Main Results:

  • The Hybrid LBP-PDC method achieved a maximum classification accuracy of 97.55%.
  • LightGBM and CatBoost classifiers, when optimized, demonstrated superior performance in glaucoma detection.
  • The study successfully developed a robust and efficient methodology for automatic glaucoma screening.

Conclusions:

  • The proposed Hybrid LBP-PDC method is effective for automatic glaucoma screening using retinal fundus images.
  • Machine learning classifiers, particularly optimized LightGBM and CatBoost, show significant potential in enhancing diagnostic accuracy.
  • This approach offers a promising tool for early detection and management of glaucoma, reducing the burden of blindness.