Detection of Glaucoma and Diabetic Retinopathy Using Machine Learning

Authors

  • S. Manasa Professor, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India
  • R. Nayana Student, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India
  • K. B. Nishchitha Student, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India
  • S. Ramya Student, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India
  • S. Sahana Student, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India

Keywords:

Machine learning, Glaucoma, Diabetic retinopathy, Open CV, Convolution neural network, Deep learning

Abstract

Among the most frequent metabolic disorders (DMs), Glaucoma relates to the vision of the human eye. This disease is irreversible, affecting the vison. Diabetes mellitus is one of the most common DMs, affecting the quality of life. DM associated with secondary complications, which can lead to vision loss, including diabetic retinopathy (DR). DL models have been developed to detect glaucoma and diabetic retinopathy. Using the convolutional neural network (CNN)architecture, the paper proposes a methodology for correct glaucoma and diabetic retinopathy detection based on deep learning (DL). A CNN can be used to figure out the difference between the patterns formed.in order to differentiate images, the CNN creates a hierarchical structure. Six layers of evaluation can be applied to the proposed work.

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Published

10-06-2022

How to Cite

[1]
S. Manasa, R. Nayana, K. B. Nishchitha, S. Ramya, and S. Sahana, “Detection of Glaucoma and Diabetic Retinopathy Using Machine Learning”, IJRESM, vol. 5, no. 6, pp. 36–38, Jun. 2022.

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Articles