Abstract
The glaucoma is an eye disease that can lead to blindness if it is not detected and treated on time. It is often associated with the increase in the intraocular pressure (IOP) of the fluid (known as aqueous humor) in the eye, and it has been nick-named the ‘Silent Thief of Sight’. In glaucoma, the optic nerve can be affected directly, and in such a case, it may be led to permanent or progressive loss of vision. Clinical treatment point of view and detection of the glaucoma at an early stage can be very helpful. The manual study of the ophthalmic images is a time-consuming process. Unlike existing methods, CNN will automatically extract the features from raw images, that can finally use to train the classifier, in which the classifier can classify the images into their respective abnormalities. In this article, deep convolutional neural network is suggested, which can recognize the multi-faceted features through apply image processing techniques on the digital fundus images of the eye for the analysis of glaucoma and normal eye.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
J. Phu, S.K. Khuu, A. Agar, I. Domadious, A. Ng, M. Kalloniatis, Visualizing the Consistency of clinical characteristics that distinguish healthy persons, glaucoma suspect patients, and manifest glaucoma patients. Ophthalmol. Glaucoma 3(4), 274–287 (2020)
G.A.K. Omodaka, K. Hashimoto, S. Tsuda, Y. Shiga, N. Takada, T. Kikawa, H. Yokota, M. Akiba, Glaucoma diagnosis with machine learning based on optical coherence tomography and color fundus images. J. Healthcare Eng. (2019)
Y.C. Tham, X. Li, T.Y. Wong, H.A. Quigley, T. Aung, C.Y. Cheng, Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121(11), 2081–90 (2014). https://doi.org/10.1016/j.ophtha.2014.05.013 (Epub 2014, PMID: 24974815)
A. Septiarini et al., Automated detection of retinal nerve fiber layer by texture-based analysis for glaucoma evaluation. Healthc. Inform. Res. 24(4), 335–345 (2018). https://doi.org/10.4258/hir.2018.24.4.335
R. Sharma, P. Sircas, et al., Automated Glaucoma detection using center slice of higher order statistics. J. Mech. Med. Biol. 19(01), 1940011. https://doi.org/10.1142/S0219519419400116
R. Zhao, X. Chen, L. Xiyao, C. Zailiang, F. Guo, S. Li, Direct cup-to-disc ratio estimation for glaucoma screening via semi-supervised learning. IEEE J. Biomed. Health Inform.
A. Septiarini, D.M. Khairina, A.H. Kridalaksana, H. Hamdani, Automatic glaucoma detection method applying a statistical approach to fundus images. Healthc Inform. Res. 24(1), 53–60 (2018). https://doi.org/10.4258/hir.2018.24.1.53
G. Pavithra, G. Anushree, T.C. Manjunath, D. Lamani, Glaucoma detection using IP techniques, in 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai (2017), pp. 3840–3843
N.A. Diptu et al., Early detection of glaucoma using fuzzy logic in Bangladesh context, in 2018 International Conference on Intelligent Systems (IS), Funchal - Madeira, Portugal, pp. 87–93 (2018)
Retinal fundus images for glaucoma analysis: the RIGA dataset, University of Michigan—Deep Blue Data. https://doi.org/10.7302/Z23R0R2
A. Budai, R. Bock, A. Maier, J. Hornegger, G. Michelson, Robust vessel segmentation in fundus images. Int. J. Bio-med. Imag. (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Karnam, A., Gidwani, H., Chirgaiya, S., Sukheja, D. (2022). Deep Neural Networks Model to Detection Glaucoma in Prima Phase. In: Reddy, A.B., Kiranmayee, B., Mukkamala, R.R., Srujan Raju, K. (eds) Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-7389-4_45
Download citation
DOI: https://doi.org/10.1007/978-981-16-7389-4_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7388-7
Online ISBN: 978-981-16-7389-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)