Skip to main content
SpringerLink
Log in

Cloud-Based Glaucoma Diagnosis in Medical Imaging Using Machine Learning

  • Chapter
  • First Online:
Artificial Intelligence for Innovative Healthcare Informatics

Abstract

The eye is one of the best gifts mankind has ever had. Technically, it can be termed as one of the sensors of our body. Glaucoma is an eye disease that is the second most cause of blindness worldwide. Being an initially unnoticeable disorder, glaucoma will cause an irreversible vision loss by the time it is realized by the patient through vision difficulties. Glaucoma has no symptoms during its early stages which makes it more dangerous. This disease affects the drainage of aqueous fluids produced inside the eye and floods the channel by narrowing down or blocking the channel through which it flows. This chapter proposed a cloud environment-based glaucoma diagnosis using Machine Learning (ML) in medical imaging. Optical coherence tomography and ML are aimed to simplify the process of glaucoma detection at early stages using a classifier deployed in the cloud architecture. This early detection will be of great use to the patients of this disease worldwide as it makes things happen artificially and automatically accurately.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tong Y, Lu W, Yu Y, Shen Y (2020) Application of machine learning in ophthalmic imaging modalities. Eye Vision 7:1–15

    Article  Google Scholar 

  2. An G, Omodaka K, Hashimoto K, Tsuda S, Shiga Y, Takada N, Nakazawa T (2019) Glaucoma diagnosis with machine learning based on optical coherence tomography and color fundus images. J Healthc Eng 2019:4061313

    Article  Google Scholar 

  3. Sarah I, Soundarya K, Tamil Thendral S, Dhanalakshmi R, Deenadayalan T (2020) DYS-I-CAN: an aid for the dyslexic to improve the skills using mobile application. In: 2020 International conference on system, computation, automation, and networking (ICSCAN). IEEE, New York, pp 1–5. https://doi.org/10.1109/ICSCAN49426.2020.9262375

  4. Kumar VA, Sharmila S, Kumar A, Bashir AK, Rashid M, Gupta SK, Alnumay WS (2021) A novel solution for finding postpartum hemorrhage using fuzzy neural techniques. Neural Comput Applic 2021:1–14

    Google Scholar 

  5. Rashid M, Singh H, Goyal V (2020) The use of machine learning and deep learning algorithms in functional magnetic resonance imaging-a systematic review. Expert Syst 37(6):e12644

    Article  Google Scholar 

  6. Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY (2014) The global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121(11):2081–2090

    Article  Google Scholar 

  7. https://www.insightvisioncenter.com/treating-and-controlling-glaucoma/

  8. Shafiq M, Tian Z, Bashir AK, Du X, Guizani M (2020) CorrAUC: a malicious bot-IoT traffic detection method in IoT network using machine-learning techniques. IEEE Internet Things J 8(5):3242–3254

    Article  Google Scholar 

  9. Kim YK, Jeoung JW, Park KH (2017) Inferior macular damage in glaucoma: its relationship to retinal nerve fiber layer defect in macular vulnerability zone. J Glaucoma 26(2):126–132

    Article  Google Scholar 

  10. Ramakrishnan N (2020) An intelligent system for early detection of eye diseases that lead to irreversible vision loss

    Google Scholar 

  11. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y (2017) Artificial intelligence in healthcare: past, present, and future. Stroke Vasc Neurol 2(4)

    Google Scholar 

  12. Pandey A, Patre P, Minj J (2020) Detection of glaucoma disease using image processing, soft computing, and deep learning approaches. In: 2020 Fourth international conference on I-SMAC (IoT in social, mobile, analytics, and cloud) (I-SMAC). IEEE, New York, pp 1–7

    Google Scholar 

  13. Salam AA, Akram MU, Arouj A, Basit I, Shaqur T, Javed H, Wazir K (2017) Benchmark data set for glaucoma detection with an annotated cup to disc ratio. In: 2017 International conference on signals and systems (ICSigSys). IEEE, New York, pp 227–233

    Chapter  Google Scholar 

  14. Devi TS, Dhanalakshmi R, Sankar S (2020) An improved framework for sentiment analysis for college reviews. Int J Adv Trends Comput Sci Eng 9(2):1959–1963, 162.

    Article  Google Scholar 

  15. Salam AA, Khalil T, Akram MU, Jameel A, Basit I (2016) Automated detection of glaucoma using structural and non-structural features. Springerplus 5(1):1–21

    Article  Google Scholar 

  16. Salam AA, Akram MU, Wazir K, Anwar SM (2015) A review analysis on early glaucoma detection using structural features. In: 2015 IEEE international conference on imaging systems and techniques (IST). IEEE, New York, pp 1–6

    Google Scholar 

  17. Anand J, Flora TA, Philip AS (2013) Finger-vein-based biometric security system. Int J Res Eng Technol. eISSN: 2319-1163

    Google Scholar 

  18. Rashid M, Singh H, Goyal V (2021) Efficient feature selection technique based on fast Fourier transform with PSO-GA for functional magnetic resonance imaging. In: 2021 2nd International conference on computation, automation and knowledge management (ICCAKM). IEEE, New York, pp 238–242

    Chapter  Google Scholar 

  19. Al-Bander B, Williams BM, Al-Taee MA, Al-Nuaimy W, Zheng Y (2017) A novel choroid segmentation method for retinal diagnosis using deep learning. In: 2017 10th International conference on developments in esystems engineering (DeSE). IEEE, New York, pp 182–187

    Chapter  Google Scholar 

  20. Ayub J, Ahmad J, Muhammad J, Aziz L, Ayub S, Akram U, Basit I (2016) Glaucoma detection through the optic disc and cup segmentation using K-mean clustering. In: 2016 International conference on computing, electronic and electrical engineering (ICE Cube). IEEE, New York, pp 143–147

    Chapter  Google Scholar 

  21. Bock R, Meier J, Michelson G, Nyúl LG, Hornegger J (2007) Classifying glaucoma with image-based features from fundus photographs. In: Joint pattern recognition symposium. Springer, Berlin, pp 355–364

    Chapter  Google Scholar 

  22. Kumaran N, Rangaraj V, Siva Sharan S, Dhanalakshmi R (2020) Intelligent personal assistant–Implementing voice commands enabling speech recognition. In: 2020 International conference on system, computation, automation and networking (ICSCAN), pp. 1–5, https://doi.org/10.1109/ICSCAN49426.2020.9262279

  23. Nawaldgi S (2016) Review of automated glaucoma detection techniques. In: 2016 International conference on wireless communications, signal processing and networking (WiSPNET). IEEE, New York, pp 1435–1438

    Chapter  Google Scholar 

  24. Thangaraj V, Natarajan V (2017) Glaucoma diagnosis using support vector machine. In: 2017 International conference on intelligent computing and control systems (ICICCS). IEEE, New York, pp 394–399

    Chapter  Google Scholar 

  25. Li F, Song D, Chen H, Xiong J, Li X, Zhong H, Ting DS (2020) Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection. NPJ Dig Med 3(1):1–8

    Article  Google Scholar 

  26. Civit-Masot J, Domínguez-Morales MJ, Vicente-Díaz S, Civit A (2020) Dual machine-learning system to aid glaucoma diagnosis using disc and cup feature extraction. IEEE Access 8:127519–127529

    Article  Google Scholar 

  27. Ker J, Wang L, Rao J, Lim T (2017) Deep learning applications in medical image analysis. IEEE Access 6:9375–9389

    Article  Google Scholar 

  28. Asri H, Mousannif H, Al Moatassime H, Noel T (2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Proc Comput Sci 83:1064–1069

    Article  Google Scholar 

  29. Civit-Masot J, Luna-Perejon F, Vicente-Diaz S, Corral JMR, Civit A (2019) TPU cloud-based generalized U-Net for eye fundus image segmentation. IEEE Access 7:142379–142387

    Article  Google Scholar 

  30. Mondal S, Agarwal K, Rashid M (2019) Deep learning approach for automatic classification of x-ray images using convolutional neural network. In: 2019 Fifth international conference on image information processing (ICIIP). IEEE, New York, pp 326–331

    Chapter  Google Scholar 

  31. Gull S, Loan NA, Parah SA (2020) An efficient watermarking technique for tamper detection and localization of medical images. J Ambient Intell Human Comput 11:1799–1808

    Article  Google Scholar 

  32. Shah AA, Parah SA, Rashid M, Elhoseny M (2020) Efficient image encryption scheme based on the generalized logistic map for real-time image processing. J Real-Time Image Proc 17(6):2139–2151

    Article  Google Scholar 

  33. Gull S, Mansour RF, Aljehane NO, Parah SA (2021) A self-embedding technique for tamper detection and localization of medical images for smart-health. Multimed Tools Appl 80(19):29939–29964

    Article  Google Scholar 

  34. Hussan M, Parah SA, Gull S, Qureshi GJ (2021) Tamper detection and self-recovery of medical imagery for smart health. Arab J Sci Eng 46(4):3465–3481

    Article  Google Scholar 

  35. Rashid M, Singh H, Goyal V (2019) Cloud storage privacy in health care systems based on IP and geo-location validation using K-mean clustering technique. Int J E-Health Med Commun 10(4):54–65

    Article  Google Scholar 

  36. Aljuaid H, Parah SA (2021) Secure patient data transfer using information embedding and hyperchaos. Sensors 21(1):282

    Article  Google Scholar 

  37. Anandh R, Indirani G (2020) IoT and cloud-based feature extraction and classification model for automatic glaucoma detection. Int J Emerg Technol 11(2):13–18

    Google Scholar 

  38. Sarosh P, Parah SA, Bhat GM, Heidari AA, Muhammad K (2021) Secret sharing-based personal health records management for the Internet of health things. Sustain Cities Soc 74:103129

    Article  Google Scholar 

  39. Dhanalakshmi R, Sridevi T (2020) Adaptive cognitive intelligence in analyzing employee feedback using LSTM. J Intell Fuzzy Syst 39(6):8069–8078

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, KCG College of Technology, Chennai, India

    R. Dhanalakshmi

  2. Department of Electronics and Communication Engineering, KCG College of Technology, Chennai, India

    Jose Anand

  3. Department of Computer Science and Engineering, Hindustan Institute of Science and Technology, Chennai, India

    K. Poonkavithai

  4. School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia

    V. Vijayakumar

Authors
  1. R. Dhanalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jose Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Poonkavithai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. Vijayakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. PG Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India

    Shabir Ahmad Parah

  2. Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India

    Mamoon Rashid

  3. University of New South Wales, Sydney, NSW, Australia

    Vijayakumar Varadarajan

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Dhanalakshmi, R., Anand, J., Poonkavithai, K., Vijayakumar, V. (2022). Cloud-Based Glaucoma Diagnosis in Medical Imaging Using Machine Learning. In: Parah, S.A., Rashid, M., Varadarajan, V. (eds) Artificial Intelligence for Innovative Healthcare Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-96569-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-96569-3_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96568-6

  • Online ISBN: 978-3-030-96569-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation