Review of Glaucoma Disease Diagnosis-based Deep Learning Network


  • Rawaa Humam Aziz AL-Qadisiyah University, College of Computer Science and Information Technology, Computer Science Department, Diwaniyah, Iraq
  • Lamia Abed Noor Muhammed AL-Qadisiyah University, College of Computer Science and Information Technology, Computer Science Department, Diwaniyah, Iraq



2-semi-Bounded operators, complete spaces, continuous and linear functions.


The glaucoma is a disease that leads to the irreversible loss of sight after its onset. In the area of medicine, glaucoma diagnosis is a crucial problem that must be addressed. Only a few studies were carried out with a view to early detection of blue. However, the implementation of this law has not succeeded in identifying glaucoma using the many methods currently available. Moreover, the old ways to identify retrograde gland disease require a much greater time commitment. Knowledge of glaucoma in colored images (fundus) is a difficult task that requires years of experience as well as knowledge. The preferred method of analyzing medical images soon became deep educational algorithms. A series of studies have been undertaken within the time period between 2018 and 2023 and these reports have shown how critical elements, such as architecture, size of data sets, and application of transport learning compared with the newly established structures, can affect performance more accurately and time-consumingly using a set of new networks (CNN).


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How to Cite

Humam Aziz, R., & Abed Noor Muhammed, L. (2024). Review of Glaucoma Disease Diagnosis-based Deep Learning Network . Journal of Al-Qadisiyah for Computer Science and Mathematics, 16(2), Comp Page 151– 160.



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