article

Automated Framework for Screening of Glaucoma Through Cloud Computing

Authors:

Malay Kishore DuttaAuthors Info & Claims

Journal of Medical Systems, Volume 43, Issue 5

Pages 1 - 17

https://doi.org/10.1007/s10916-019-1260-2

Published: 01 May 2019 Publication History

Abstract

In recent times, the use of computer aided diagnosis for detection of Glaucoma from fundus images has been prevalent. In the proposed work, a cloud based system is proposed for automatic and real-time screening of Glaucoma with the use of automatic image processing techniques. The proposed system offers scalability to the developers and easy accessibility to the consumers. The proposed system is device and location independent. The input digital image is analyzed and a comprehensive diagnostic report is generated consisting of detailed analysis of indicative medical parameters like optic-cup-to-disc ratio, optic neuro-retinal rim, ISNT rules making the report informative and clinically significant. With recent advances in the field of communication technologies, the internet facilities are available that make the proposed system an efficient and economical method for initial screening and offer preventive and diagnostic steps in early disease intervention and management. The proposed system can perform an initial screening test in an average time of 6 s on high resolution fundus images. The proposed system has been tested on a fundus database and an average sensitivity of 93.7% has been achieved for Glaucoma cases. In places where there is scarcity of trained ophthalmologists and lack of awareness of such diseases, the cloud based system can be used as an effective diagnostic assistive tool.

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Cited By

Ubochi BOlawumi AMacaulay JAyomide OAkingbade K(2024)Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma DetectionJournal of Electrical and Computer Engineering10.1155/2024/80531172024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/8053117
Garg HGupta NAgrawal RShivani SSharma B(2022)A real time cloud-based framework for glaucoma screening using EfficientNetMultimedia Tools and Applications10.1007/s11042-021-11559-881:24(34737-34758)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11042-021-11559-8
Gupta NGarg HAgarwal R(2022)A robust framework for glaucoma detection using CLAHE and EfficientNetThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02114-538:7(2315-2328)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s00371-021-02114-5

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Automated Framework for Screening of Glaucoma Through Cloud Computing

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Published In

cover image Journal of Medical Systems

Journal of Medical Systems Volume 43, Issue 5

May 2019

413 pages

ISSN:0148-5598

Issue’s Table of Contents

Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Plenum Press

United States

Publication History

Published: 01 May 2019

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Cited By

Ubochi BOlawumi AMacaulay JAyomide OAkingbade K(2024)Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma DetectionJournal of Electrical and Computer Engineering10.1155/2024/80531172024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/8053117
Garg HGupta NAgrawal RShivani SSharma B(2022)A real time cloud-based framework for glaucoma screening using EfficientNetMultimedia Tools and Applications10.1007/s11042-021-11559-881:24(34737-34758)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11042-021-11559-8
Gupta NGarg HAgarwal R(2022)A robust framework for glaucoma detection using CLAHE and EfficientNetThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02114-538:7(2315-2328)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s00371-021-02114-5
Patil NPatil PRao P(2021)RETRACTED ARTICLE: Convolution neural network and deep-belief network (DBN) based automatic detection and diagnosis of GlaucomaMultimedia Tools and Applications10.1007/s11042-021-11087-580:19(29481-29495)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11042-021-11087-5

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