Detection of Glaucoma using Deep Learning

Authors

  • Ravi Kumar Gupta Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, India
  • Utkarsh Sharma Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, India
  • Vivek Singh Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, India

Keywords:

area under curve, digital fundus image, region of interest, optic disc, cup disc ratio

Abstract

Glaucoma is an incurable disease that impairs vision and livability. We used a convolutional neural network to construct a deep learning framework for the identification of spontaneous eye problem in this study. In-depth instructional strategies, like as convolutional neural networks, might evaluate visual sequences in which glaucoma as well as non-glaucoma characteristics may be segregated for testing objectives. The suggested Deep Learning architecture comprise of six study layers: four conv layers and two completely functional levels. Discontinuation and augmentation methods are utilized to increase the accuracy of glaucoma diagnosis. ORIGA and SCES details have been thoroughly tested.

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Published

22-05-2022

Issue

Section

Articles

How to Cite

[1]
R. K. Gupta, U. Sharma, and V. Singh, “Detection of Glaucoma using Deep Learning”, IJRESM, vol. 5, no. 5, pp. 147–150, May 2022, Accessed: Jul. 13, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/2072