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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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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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Leveraging Mask Autoencoder and Crossover Binary Sand Cat Algorithm for Early Detection of Glaucoma.

C Rekha1, K Jayashree2

  • 1Department of Information Technology, Panimalar Engineering College, Chennai, India.

Microscopy Research and Technique
|February 17, 2025
PubMed
Summary

A novel mask autoencoder-based crossover binary sand cat (MA-CBSC) algorithm effectively detects glaucoma from retinal images. This automated system achieves 98.3% accuracy, aiding early diagnosis and preventing irreversible blindness.

Keywords:
autoencoderbinary sand cat optimizationcrossover strategyfiltersfundus imageglaucomaimage enhancementmask RCNN

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Area of Science:

  • Ophthalmology and Computer Vision
  • Medical Image Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Glaucoma is a primary cause of irreversible blindness, often linked to aging and requiring early detection for management.
  • Automated glaucoma detection from retinal fundus images is crucial, but image quality variations pose significant challenges.
  • Interdisciplinary collaboration is vital for developing robust glaucoma detection solutions.

Purpose of the Study:

  • To develop and evaluate a novel automated system for glaucoma detection using retinal fundus images.
  • To address challenges in image quality and feature extraction for improved diagnostic accuracy.
  • To enhance the performance of glaucoma detection algorithms through advanced machine learning techniques.

Main Methods:

  • A mask autoencoder-based crossover binary sand cat (MA-CBSC) algorithm was proposed for glaucoma detection.
  • Image enhancement, filtering, and data cleaning were applied to extracted Regions of Interest (ROI).
  • Hyperparameter tuning using the crossover-based binary sand cat optimization algorithm refined the MA-CBSC method's efficiency.

Main Results:

  • The MA-CBSC algorithm demonstrated superior performance compared to existing techniques on multiple datasets.
  • Key performance metrics including accuracy, precision, F1 score, sensitivity, and specificity were evaluated.
  • The proposed method achieved a high accuracy rate of 98.3% in glaucoma detection.

Conclusions:

  • The MA-CBSC algorithm offers a robust and accurate automated solution for glaucoma screening and diagnosis.
  • The developed system assists ophthalmologists by providing reliable detection from retinal fundus images.
  • This approach holds promise for early intervention and mitigating glaucoma-induced vision loss.