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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

528
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 Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma
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Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus

M Shanmuga Eswari1, S Balamurali1, Lakshmana Kumar Ramasamy2

  • 1Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India.

The Journal of International Medical Research
|September 20, 2024
PubMed
Summary

An optimized decision support system using artificial algae algorithm with support vector machine (AAASVM) achieved 96.52% accuracy for glaucoma screening from retinal fundus images. This computer-aided system aids optometrists in patient progress assessment.

Keywords:
TernausNetartificial algae algorithmfaster region-based convolutional neural networkfundusglaucomascreeningsupport vector machine

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Area of Science:

  • Ophthalmology
  • Computer Vision
  • Artificial Intelligence

Background:

  • Glaucoma screening from retinal fundus images is crucial for early detection and prevention of vision loss.
  • Existing methods may lack the accuracy and efficiency required for widespread clinical application.

Purpose of the Study:

  • To develop and optimize a decision support system for glaucoma screening using retinal fundus images.
  • To enhance the accuracy and efficiency of glaucoma detection through advanced computer vision and machine learning techniques.

Main Methods:

  • Combined computer vision algorithms with convolutional neural networks (CNNs) for fundus image analysis.
  • Utilized a faster region-based convolutional neural network (FRCNN) and an artificial algae algorithm with support vector machine (AAASVM) classifier.
  • Implemented optic boundary detection, optic cup, and optic disc segmentation using TernausNet.

Main Results:

  • Achieved high accuracy rates across three datasets: 95.11% (G1020), 92.87% (DRIEV), and 93.7% (high-resolution fundus).
  • The optimized FRCNN (AFRCNN) demonstrated an average accuracy of 94.06%, with 93.353% sensitivity and 94.706% specificity.
  • The AAASVM classifier outperformed FRCNN, achieving an average accuracy of 96.52% and AUCs of 0.9, 0.85, and 0.87 for the respective datasets.

Conclusions:

  • The AAASVM model proved to be the most effective for glaucoma screening based on statistical Friedman evaluation.
  • The system's ability to segment and classify images facilitates patient progress monitoring within healthcare systems.
  • This computer-aided decision support system offers significant utility for optometrists in glaucoma management.