Glauco-Retino Detection using Non-invasive Glucometer

Swati G Shanbhag, Monika D, Mallamma, Poojashree K, Manjula G Hegde

Abstract— Glaucoma is an eye disease which the optic nerve of the eye gets damaged and becomes severe over time. It is characterized by elevated intraocular pressure. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma management. The early detection of glaucoma is important in order to enable appropriate monitoring, treatment and to minimize the risk of irreversible visual field loss. In this proposed method, the structural features like cup-to-disk ratio, neuro-retinal rim and textural hybrid features are considered, then analysed to classify as glaucomatous image. The required feature for the calculation of optic cup and disc ratio are extracted using segmentation in image processing. Energy distribution over wavelet sub bands will be applied to find the textural region of interest. Finally, extracted features are applied to multiple Machine learning algorithms for the effective classification by considering normal Subject’s extracted features. Diabetic retinopathy, which is also known as diabetic eye disease (DED), is a medical condition in which damage occurs to the retina due to diabetes mellitus. It is a leading cause of blindness in several developed countries. Keywords - Glaucoma, Machine Learning Techniques, Deep Learning, Fundus images, Convolutional Neural Network

Title:	Glauco-Retino Detection using Non-invasive Glucometer
Author Name:	Swati G Shanbhag, Monika D, Mallamma, Poojashree K, Manjula G Hegde
Abstract:	Abstract— Glaucoma is an eye disease which the optic nerve of the eye gets damaged and becomes severe over time. It is characterized by elevated intraocular pressure. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma management. The early detection of glaucoma is important in order to enable appropriate monitoring, treatment and to minimize the risk of irreversible visual field loss. In this proposed method, the structural features like cup-to-disk ratio, neuro-retinal rim and textural hybrid features are considered, then analysed to classify as glaucomatous image. The required feature for the calculation of optic cup and disc ratio are extracted using segmentation in image processing. Energy distribution over wavelet sub bands will be applied to find the textural region of interest. Finally, extracted features are applied to multiple Machine learning algorithms for the effective classification by considering normal Subject’s extracted features. Diabetic retinopathy, which is also known as diabetic eye disease (DED), is a medical condition in which damage occurs to the retina due to diabetes mellitus. It is a leading cause of blindness in several developed countries. Keywords - Glaucoma, Machine Learning Techniques, Deep Learning, Fundus images, Convolutional Neural Network
Cite this article:	Swati G Shanbhag, Monika D, Mallamma, Poojashree K, Manjula G Hegde , " Glauco-Retino Detection using Non-invasive Glucometer " , International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 11, Issue 7, July 2022 , pp. 001-006 , ISSN 2319 - 4847.
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