Exploring Deep Learning Techniques for Glaucoma Detection: A Comprehensive Review


  • Aized Amin Soofi Department of Computer Science, National University of Modern Languages, Faisalabad, Pakistan.
  • Fazal-e-Amin Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, KSA.


Glaucoma Detection, Deep learning techniques, Medical Imaging, Glaucoma Segmentation, Comprehensive Survey, Machine Learning


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.




How to Cite

Aized Amin Soofi, & Fazal-e-Amin. (2023). Exploring Deep Learning Techniques for Glaucoma Detection: A Comprehensive Review. Journal of Computing & Biomedical Informatics, 5(02), 220–238. Retrieved from https://jcbi.org/index.php/Main/article/view/192