Image Processing Based Disease Detection Concepts Using Multi SVM Techniques

Abstract

In this paper, we present the image processing-based glaucoma detection using multi SVM techniques are presented in a nutshell. The human eye is one of the body's most important organs. The eye is always important in our everyday lives; without eyes, the world would be dark, and performing daily tasks would be extremely difficult. In the sense that without sight, it would be extremely difficult for someone to perform any task. The loss of vision/sight in the human eyes can be caused by a variety of factors. As a result, blindness in the human eyes must be prevented, as the most valuable human organ is solely responsible for vision. Different forms of diseases that occur in the eyes as a result of multiple factors are one of the causes of blindness and vision loss in the eyes. Glaucoma is a condition that develops as a result of vision loss. A Convolutional Neural Network (CNN) is proposed in this approach for detecting glaucoma from fundus images of the eyes. We use the k-means algorithm for segmentation, GLCM for feature extraction, and Multi-SVM for classification in the proposed algorithm (Support Vector Machine).