Acta Scientific Ophthalmology (ISSN: 2582-3191)

Research Article Volume 5 Issue 12

Image Processing Based Types of Chronic Ailments of the Human Eyes for Glaucomatic Disease Detection Using KNN Techniques

Mahesh B Neelagar1, Balaji KA2, Pavithra G3 and TC Manjunath4*

1Assistant Professor, ECE, Department of PG Studies (VLSIDES), VTU, Belagavi, India
2Assistant Professor, School of Electronics and Communication Engineering, Presidency University, Bangalore, India
3Associate Professor, ECE Department, Dayananda Sagar College of Engineering, Bangalore, India
4Professor and HOD, ECE Department, Dayananda Sagar College of Engineering, Bangalore, India

*Corresponding Author: TC Manjunath, Professor and HOD, ECE Department, Dayananda Sagar College of Engineering, Bangalore, India.

Received: October 14, 2022; Published: November 23, 2022

Abstract

In this research paper, the Image Processing Based Glaucoma Detection Using KNN Techniques – a prototype is being presented in a nutshell. The human eye is one of the most essential organs in the body. The eye is continuously vital in our daily lives; without them, the world would be dark and doing daily activities would be exceedingly difficult. In the sense that it would be exceedingly difficult for anyone to accomplish any work without sight. The loss of vision/sight in the human eyes can be caused by a number of reasons. As a result, blindness in the human eyes must be avoided, as the most valued human organ is totally responsible for seeing. One of the reasons of blindness and vision loss in the eyes is various types of diseases that develop in the eyes as a consequence of a variety of conditions. A Convolutional Neural Network (CNN) is proposed in this approach for detecting glaucoma using fundus pictures of the eyes. We utilise the Otsu thresholding approach for segmenting, followed by HOG feature extraction techniques and Knn algorithm classification. For training and testing the model, we utilise a Convolution Neural Network.

Keywords: Fundus Images; Glaucoma; Retinal Fundus Image; Convolution Neural Network

References

  1. Kar Sudeshna Sil and Santi P Maity. “Automatic detection of retinal lesions for screening of diabetic retinopathy”. IEEE Transactions on Biomedical Engineering 65.3 (2018): 608-618.
  2. X Chen., et al. “Glaucoma detection based on deep convolutional neural network”. Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milano, Italy, August (2015).
  3. Szegedy Christian., et al. “Rethinking the inception architecture for computer vision”. Proceedings of the IEEE conference on computer vision and pattern recognition (2016).
  4. Dean Jeffrey., et al. “Large scale distributed deep networks”. Advances in Neural Information Processing Systems (2012).
  5. Rakhlin Alexander., et al. “Diabetic Retinopathy detection through integration of Deep Learning classification framework”. bioRxiv (2018).
  6. Krizhevsky Alex., et al. “Imagenet classification with deep convolutional neural networks”. Advances in Neural Information Processing Systems (2012).
  7. Simonyan Karen and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition”. arXiv preprint arXiv:1409.1556, (2014).
  8. Doshi Darshit., et al. “Diabetic retinopathy detection using deep convolutional neural networks”. International Conference on Computing, Analytics and Security Trends (CAST). IEEE (2016).
  9. Ojala Timo., et al. “A comparative study of texture measures with classification based on featured distributions”. Pattern Recognition 29.1 (1996): 51-59.

Citation

Citation: TC Manjunath., et al. “Image Processing Based Types of Chronic Ailments of the Human Eyes for Glaucomatic Disease Detection Using KNN Techniques".Acta Scientific Ophthalmology 5.12 (2022): 37-40.

Copyright

Copyright: © 2022 TC Manjunath., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days
ISI- IF1.042
JCR- IF0.24

Indexed In




News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is November 25, 2024.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of "Best Article of the Issue"
  • Welcoming Article Submission
    Acta Scientific delightfully welcomes active researchers for submission of articles towards the upcoming issue of respective journals.

Contact US