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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

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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Open Angle Glaucoma: Treatment01:27

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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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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier

Vijaya Kumar Velpula1, Lakhan Dev Sharma1

  • 1School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.

Frontiers in Physiology
|June 29, 2023
PubMed
Summary

This study developed an automated glaucoma detection system using deep convolutional neural networks (CNNs) and classifier fusion. The system achieves high accuracy for early glaucoma detection from fundus images, outperforming existing methods.

Keywords:
classifier fusionconvolutional neural networkdeep learningfundus imagehybrid modeltransfer learning

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a leading cause of irreversible blindness.
  • Early detection is critical for effective glaucoma management.
  • Current diagnostic methods are time-consuming and can be inaccurate.

Purpose of the Study:

  • To develop an automated system for early glaucoma detection.
  • To classify glaucoma stages using deep learning models.
  • To enhance diagnostic accuracy through classifier fusion.

Main Methods:

  • Utilized five pre-trained deep convolutional neural network (CNN) models: ResNet50, AlexNet, VGG19, DenseNet-201, and Inception-ResNet-v2.
  • Employed a maximum voting-based classifier fusion approach.
  • Validated the model on four public datasets: ACRIMA, RIM-ONE, Harvard Dataverse (HVD), and Drishti.

Main Results:

  • Achieved 99.57% accuracy and an AUC of 1.0 on the ACRIMA dataset.
  • Demonstrated high performance across datasets, with AUCs of 0.97 (HVD), 0.90 (Drishti), and 0.95 (RIM-ONE).
  • The proposed model outperformed state-of-the-art methods in early-stage glaucoma classification.

Conclusions:

  • The automated glaucoma classification model effectively enables early glaucoma detection.
  • The combination of pre-trained CNNs and classifier fusion offers superior performance.
  • The system shows promise for improving glaucoma diagnosis accuracy and efficiency.