Skip to main content
SpringerLink
Log in

Retinopathy Detection Using Probabilistic Neural Network

  • Conference paper
  • First Online:
Advances in Information Communication Technology and Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 135))

  • 734 Accesses

Abstract

We propose a diabetic retinopathy (DR) analysis algorithm based on probabilistic neural network (PNN). This algorithm is used to recognize the pattern problem. By this algorithm, we can help in diagnosis of a diabetic patient regarding their damage to the back of retina (eye) occurred in tissue of blood vessels using probabilistic neural network. PNN is also known as feed forward neural network. This algorithm has been tested on a small image database and compared with the performance of a human eye. Confusion matrix and kappa coefficient are used to find the accuracy rate of the diabetic eye.

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 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Sopharak A, Uyyanonvara B, Barman S, Williamson TH (2008) Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graph 32(8):720–727

    Article  Google Scholar 

  2. Schaefer G, Leung E (2007) Neural networks for exudate detection in retinal images. In: International symposium on visual computing. Springer, Berlin, Heidelberg, pp 298–306

    Google Scholar 

  3. Solanki MS (2015) Diabetic retinopathy detection using eye images. Artif Intell Course Project 1–10

    Google Scholar 

  4. Doshi D, Shenoy A, Sidhpura D, Gharpure P (2016) Diabetic retinopathy detection using deep convolutional neural networks. In: 2016 International conference on computing, analytics and security trends (CAST). IEEE, pp 261–266

    Google Scholar 

  5. https://www.kaggle.com/c/diabetic-retinopathy-detection

  6. De la Calleja J, Tecuapetla L, Medina MA, Bárcenas E, Nájera ABU (2014) LBP and machine learning for diabetic retinopathy detection. In: International conference on intelligent data engineering and automated learning. Springer, Cham, pp 110–117

    Google Scholar 

Download references

Acknowledgements

We thank Kaggle [5] for free dataset online.

Author information

Authors and Affiliations

  1. Thakur College of Science and Commerce, Kandivali (E), Mumbai, 400101, India

    Anand Upadhyay, Parth Kantelia & Rohan Parmar

Authors
  1. Anand Upadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Parth Kantelia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rohan Parmar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anand Upadhyay .

Editor information

Editors and Affiliations

  1. Department of Computer Application, Government Engineering College, Bikaner, Rajasthan, India

    Vishal Goar

  2. Department of Electronics and Communication, Government Engineering College, Bikaner, Rajasthan, India

    Manoj Kuri

  3. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajesh Kumar

  4. Department of Electrical and Electronics Engineering, University of the Ryukyus, Nishihara, Japan

    Tomonobu Senjyu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Upadhyay, A., Kantelia, P., Parmar, R. (2021). Retinopathy Detection Using Probabilistic Neural Network. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 135. Springer, Singapore. https://doi.org/10.1007/978-981-15-5421-6_26

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-5421-6_26

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5420-9

  • Online ISBN: 978-981-15-5421-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation