GLAUCOMA DETECTION TECHNIQUES: A REVIEW

Authors

  • Sanjeev Indora Associate Professor Dept. Of Computer Science And Engineering Deenbandhu Chhotu Ram University Of Science And Technology,Murthal Sonipat ,India
  • Dinesh Kumar Atal
  • Supiksha Jain

DOI:

https://doi.org/10.37591/rrjomst.v12i2.3236

Keywords:

Glaucoma, Intraocular pressure, Convolutional Neural Network

Abstract

The eye is the second most complex and valuable organ after the brain. The second most common among eye conditions is glaucoma, an eye disease that is widespread and has the potential to cause neurodegeneration. The main cause of Glaucoma is abnormal pressure within the eye, which can be challenging to detect in the early stages since there are no clear symptoms. A timely diagnosis plays a critical role in achieving a favorable prognosis and enhancing the quality of life for the patient. Although manual eye examination is a valid method, it requires significant human effort. Glaucoma is relatively common in all races, but it is more prevalent in Africans, Americans, and Afro-Caribbeans. The aim of this review is to present a thorough examination of the various categories of glaucoma, their underlying causes, potential treatment options, publicly accessible image benchmarks, performance metrics, and different methodologies, along with their corresponding advantages and disadvantages. The results of each category are summarized using tabular representations.

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Published

2023-09-05

Issue

Vol. 12 No. 2 (2023)

Section

Review Article

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