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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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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.
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  3. Glaucoma Detection And Classification Using Improved U-net Deep Learning Model.
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  3. Glaucoma Detection And Classification Using Improved U-net Deep Learning Model.

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Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model.

Ramgopal Kashyap1, Rajit Nair2, Syam Machinathu Parambil Gangadharan3

  • 1Amity School of Engineering and Technology, Amity University Chhattisgarh, Raipur 493225, India.

Healthcare (Basel, Switzerland)
|December 23, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

A new deep learning system accurately detects glaucoma using retinal images, improving early diagnosis rates. This advanced technology offers a promising solution for identifying glaucoma before symptoms manifest, aiding global healthcare efforts.

Keywords:
DenseNet-201 modelclassificationdeep convolution neural networkimage segmentationimproved U-Net

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma affects millions globally, with significant misdiagnosis rates in developing healthcare systems.
  • Early detection of glaucoma is crucial for preventing vision loss and managing the condition effectively.
  • Current diagnostic methods can be limited, necessitating advanced technological solutions.

Purpose of the Study:

  • To develop and evaluate a deep learning system for the early detection and prediction of glaucoma.
  • To utilize retinal fundus images for identifying glaucoma and assessing its presence in patients.
  • To improve the accuracy and efficiency of glaucoma diagnosis through artificial intelligence.

Main Methods:

  • A deep learning algorithm integrating pretrained transfer learning models with the U-Net architecture was employed.
  • DenseNet-201, a deep convolutional neural network (DCNN), was used for feature extraction from retinal images.
  • The DCNN approach was applied to segment the optic cup and classify glaucoma presence.

    • Main Results:

    • The proposed deep learning model achieved high accuracy in detecting glaucoma from retinal fundus images.
    • The model demonstrated a training accuracy of 98.82% and a testing accuracy of 96.90%.
    • Comparative analysis showed the proposed model outperformed existing deep learning-based classification methods.

    Conclusions:

    • The developed deep learning system shows significant potential for accurate and early glaucoma detection.
    • This AI-driven approach can aid in timely diagnosis, especially in regions with limited healthcare infrastructure.
    • The findings suggest a more successful and reliable paradigm for glaucoma screening and diagnosis.