Exploring Deep Learning Techniques for Glaucoma Detection: A
  Comprehensive Review
Aized Amin Soofi Fazal-e-Amin
Glaucoma is one of the primary causes of vision loss around the world,
necessitating accurate and efficient detection methods. Traditional manual
detection approaches have limitations in terms of cost, time, and subjectivity.
Recent developments in deep learning approaches demonstrate potential in
automating glaucoma detection by detecting relevant features from retinal
fundus images. This article provides a comprehensive overview of cutting-edge
deep learning methods used for the segmentation, classification, and detection
of glaucoma. By analyzing recent studies, the effectiveness and limitations of
these techniques are evaluated, key findings are highlighted, and potential
areas for further research are identified. The use of deep learning algorithms
may significantly improve the efficacy, usefulness, and accuracy of glaucoma
detection. The findings from this research contribute to the ongoing
advancements in automated glaucoma detection and have implications for
improving patient outcomes and reducing the global burden of glaucoma.