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

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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.