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Prediction of Glaucoma Using Convolutional Neural Network
Subject area: Science,Engineering and Technology · Area of research: Healthcare
Abstract
Glaucoma, a leading cause of irreversible vision loss, poses a significant public health challenge worldwide. Early detection and timely intervention are crucial in managing this disease effectively. This study presents an innovative approach for the prediction of glaucoma using Convolutional Neural Networks (CNNs), a deep learning technique well-suited for image analysis tasks. This paper contributes to the development of non-invasive, cost-effective, and scalable tools for early glaucoma detection, enabling healthcare professionals to identify at-risk patients and initiate appropriate interventions promptly. The integration of deep learning techniques, particularly CNNs, showcases the potential for artificial intelligence in improving the diagnosis and management of glaucoma, ultimately preserving precious vision and enhancing the quality of life for affected individuals.
Keywords
Glaucoma, Deep Learning, Retinal Images, Early Detection
References
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[2] Akter, N., Fletcher, J., Perry, S. et al.(2022)Glaucoma diagnosis using multi- feature analysis and a deep learning technique. Sci Rep 12, 8064. https://doi.org/10.1038/s41598-022-12147-y
[3] Tabassum M, Khan TM, Arsalan M, Naqvi SS, Ahmed M, Madni HA, Mirza J (2020) CDED-Net: joint segmentation of optic disc and optic cup for glaucoma screening. IEEE Access 8:102733–102747
[4] Jibhakate.P, Gole.S, Yeskar.P, Rangwani.N, Vyas.A and Dhote.K(2022), "Early Glaucoma Detection Using Machine Learning Algorithms of VGG- 16 and Resnet-50," 2022 IEEE Region 10 Symposium (TENSYMP), Mumbai, India, pp. 1-5, doi:10.1109/TENSYMP54529.2022.9864471.
[5] Raju. M, Shanmugam. K.P.; ShyuC.R,(2023) Application of Machine Learning Predictive Models for Early Detection of Glaucoma Using Real World Data. Appl. Sci., 13, 2445. https://doi.org/10.3390/app13042445
[6] Mamta Juneja, Janmejai Singh Minhas, Naveen Singla, Sarthak Thakur, Niharika Thakur, Prashant Jindal, (2022), Fused framework for glaucoma diagnosis using Optical Coherence Tomography (OCT) images, Expert Systems with Applications, Volume 201, 117202, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.117202.
How to cite this paper
@article{1705161,
author = {Kavyasri P P, Akalyaa S, Aanandhavarsini M},
title = {Prediction of Glaucoma Using Convolutional Neural Network},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {4},
pages = {429-433},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705161.pdf},
abstract = {Glaucoma, a leading cause of irreversible vision loss, poses a significant public health challenge worldwide. Early detection and timely intervention are crucial in managing this disease effectively. This study presents an innovative approach for the prediction of glaucoma using Convolutional Neural Networks (CNNs), a deep learning technique well-suited for image analysis tasks. This paper contributes to the development of non-invasive, cost-effective, and scalable tools for early glaucoma detection, enabling healthcare professionals to identify at-risk patients and initiate appropriate interventions promptly. The integration of deep learning techniques, particularly CNNs, showcases the potential for artificial intelligence in improving the diagnosis and management of glaucoma, ultimately preserving precious vision and enhancing the quality of life for affected individuals.},
keywords = {Glaucoma, Deep Learning, Retinal Images, Early Detection},
month = {October},
}