Skip to main content
SpringerLink
Log in

FUNDUS and OCT Image Classification Using DL Techniques

  • Conference paper
  • First Online:
Rising Threats in Expert Applications and Solutions

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 434))

Abstract

The phrase “Medical Diagnosis” alludes to the means of identifying a person’s disease symptoms and signs. Decision-making in medical diagnosis is a challenging task as it requires highly specialized medical experts for interpreting the parameters and symptoms of the diseases. Different types of studies such as pathology, ophthalmology, oncology, etc. are conducted to understand the propagation of different kinds of diseases. Ophthalmology is the branch that deals with the study of the eye. The Eye is a sensitive and vital sense organ of the human body that responds to light and allows vision. For living a quality life, a good vision is a necessity of human beings. In FUNDUS and optical coherence Tomography (OCT) pictures, DL (DL) have excelled. The majority of prior research has concentrated on recognizing a specific FUNDUS and OCT illness. This work reviews various papers on the DL technique for FUNDUS and OCT classification. Several classification approaches had been communicated in the literature for the automatic classification of FUNDUS and OCT images in which DL techniques outperformed. Different DL (DL) techniques for automatic eye diseases classification had been discussed in this paper and results are compared on the basis of accuracy, F1-score, and AUC.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
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. D.S. Kermany, M. Goldbaum, W. Cai et al., Identifying medical diagnoses and treatable diseases by image-based DL. Cell 172(5), 1122-1131.e9 (2018). https://doi.org/10.1016/j.cell.2018.02.010 (PMID: 29474911)

    Article  Google Scholar 

  2. S. Kaymak, A. Serener, Automated age-related macular degeneration and diabetic Macular Edema detection on OCT Images using DL,in 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) (2018), pp.265–269. https://doi.org/10.1109/iccp.2018.8516635

  3. V. Das, S. Dandapat, P.K. Bora, Multi-scale deep feature fusion for automated classification of macular pathologies from OCT images. Biomed. Signal Process. Control 54, 101605 (2019). https://doi.org/10.1016/j.bspc.2019.101605

    Article  Google Scholar 

  4. A. Bhowmik, S. Kumar, N. Bhat, Eye disease prediction from optical coherence tomography images with transfer learning. Commun. Comput. Inf. Sci. 1000, 104–114 (2019). https://doi.org/10.1007/978-3-030-20257-6_9

    Article  Google Scholar 

  5. F. Li, H. Chen, Z. Liu, X. Zhang, Z. Wu, Fully automated detection of retinal disorders by image-based DL. Graefe’s Archive Clin. Exp. Ophthalmol. (2019). https://doi.org/10.1007/s00417018-04224-8

    Article  Google Scholar 

  6. A.M. Alqudah, AOCT-NET: A convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images. Med. Biol. Eng. Compu. 58(1), 41–53 (2019). https://doi.org/10.1007/s11517-019-02066-y

    Article  Google Scholar 

  7. L. Fang, Y. Jin, L. Huang, S. Guo, G. Zhao, X. Chen, Iterative fusion convolutional neural networks for classification of optical coherence tomography images. J. Vis. Commun. Image Represent. (2019). https://doi.org/10.1016/j.jvcir.2019.01.022

    Article  Google Scholar 

  8. T.K. Yoo, J.Y. Choi, H.K. Kim, Feasibility study to improve DL in OCT diagnosis of rare retinal diseases with few-shot classification. Med. Biol. Eng. Compu. 59(2), 401–415 (2021). https://doi.org/10.1007/s11517-021-02321-1

    Article  Google Scholar 

  9. N. Rajagopalan, A.N. Josephraj, E. Srithaladevi, Diagnosis of retinal disorders from optical coherence tomography images using CNN. PloS One 16 (7), e0254180 (2021). https://doi.org/10.1371/journal.pone.0254180

  10. A. Thomas, P.M. Harikrishnan, A.K. Krishna, K.P. Palinsamy, V.P. Gopi, Automated detection of age-related macular degeneration from OCT images using multipath CNN. J. Comput. Sci. Eng. 15(1), 34–46 (2021). https://doi.org/10.5626/JCSE.2021.15.1.34

    Article  Google Scholar 

  11. R. Ghosh, K. Ghosh, S. Maitra, Automatic detection and classification of diabetic retinopathy stages using CNN, in 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN) (2017). https://doi.org/10.1109/spin.2017.8050011

  12. M. Raju, V. Pagidimarri, R. Barreto, A. Kadam, V. Kasivajjala, A. Aswath, Development of a DL algorithm for automatic diagnosis of diabetic retinopathy. Stud. Health Technol. Inf. 245, 559–563 (2017)

    Google Scholar 

  13. S. Wan, Y. Liang, Y. Zhang, Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput. Electr. Eng. 72, 274–282 (2018). https://doi.org/10.1016/j.compeleceng.2018.07.042

    Article  Google Scholar 

  14. H. Chen, X. Zeng, Y. Luo, W. Ye, Detection of diabetic retinopathy using deep neural network, in 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) (2018). https://doi.org/10.1109/icdsp.2018.8631882

  15. G.M. Lin, M.J. Chen, C.H. Yeh, Y.Y. Lin, H.Y. Kuo, M.H. Lin, M.C. Chen, S.D. Lin, Y. Gao, A. Ran, C.Y. Cheung, Transforming retinal photographs to entropy images in DL to improve automated detection for diabetic retinopathy. Hindawi J. Ophthalmol. (2018)

    Google Scholar 

  16. U. Raghavendra, H. Fujita, S.V. Bhandary, A. Gudigar, J.H. Tan, U.R. Acharya, Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inf. Sci. 441, 41–49 (2018). https://doi.org/10.1016/j.ins.2018.01.051

    Article  MathSciNet  Google Scholar 

  17. A. Serener, S. Serte, Transfer Learning for Early and Advanced Glaucoma Detection with Convolutional Neural Networks (IEEE, 2019), pp. 271–274

    Google Scholar 

  18. Y. Sun, G. Yang, D. Ding, G. Cheng, J. Xu, X. Li, A GAN-based domain adaptation method for glaucoma diagnosis, in 2020 International Joint Conference on Neural Networks (IJCNN) (2020). https://doi.org/10.1109/ijcnn48605.2020.92073

  19. A.U. Rehman, I.A. Taj, M. Sajid, K.S. Karimov, An ensemble framework based on deep CNNs architecture for glaucoma classification using FUNDUS photography. Math. Biosci. Eng. (MBE) 18(5), 5321–5346 (2021)

    Article  MathSciNet  Google Scholar 

  20. F. Grassmann, J. Mengelkamp, C. Brandl, S. Harsch, M.E. Zimmermann, B. Linkohr et al., A DL algorithm for prediction of age-related eye disease study severity scale for age related macular degeneration from color fundus photography. Ophthalmology 125 (9), 1410–1420. (2018). https://doi.org/10.1016/j.ophtha.2018.02.037

  21. J.H. Tan, S.V. Bhandary, S. Sivaprasad, Y. Hagiwara, A. Bagchi, U. Raghavendra et al., Age-related macular degeneration detection using deep convolutional neural network. Future Gener. Comput. Syst. 87, 127–135 (2018). https://doi.org/10.1016/j.future.2018.05.001

  22. Y. Peng, S. Dharssi, Q. Chen, T.D. Keenan, E. Agrón, W.T. Wong, E.Y. Chew, Z. Lu, DeepSeeNet: A DL model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 126(4), 565–575 (2019). https://doi.org/10.1016/j.ophtha.2018.11.01

    Article  Google Scholar 

  23. T.-Y. Heo, K.M. Kim, H.K. Min, S.M. Gu, J.H. Kim, J. Yun, J.K. Min, Development of a deep-learning-based artificial intelligence tool for differential diagnosis between dry and neovascular age-related macular degeneration. Diagnostics 10(5), 261 (2020). https://doi.org/10.3390/diagnostics10050261

    Article  Google Scholar 

  24. M.T. Islam, S.A. Imran, A. Arefeen, M. Hasan, C. Shahnaz, Source and camera independent ophthalmic disease recognition from FUNDUS image using neural network, in 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON) (2019). https://doi.org/10.1109/spicscon48833.2019.9065162

  25. J. He, C. Li, J. Ye, Y. Qiao, L. Gu, Multi-label ocular disease classification with a dense correlation deep neural network. Biomed. Signal Process. Control 63, 102167 (2021). https://doi.org/10.1016/j.bspc.2020.102167

    Article  Google Scholar 

  26. N. Gour, P. Khanna, Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network. Biomed. Signal Process. Control 66, 102329 (2021). https://doi.org/10.1016/j.bspc.2020.102329

    Article  Google Scholar 

  27. J. Wang, L. Yang, Z. Huo, W. He, J. Luo, Multi-label classification of FUNDUS images with efficient net. IEEE Access 8, 212499–212508 (2020). https://doi.org/10.1109/access.2020.3040275

    Article  Google Scholar 

  28. N. Li, T. Li, C. Hu, K. Wang, H. Kang, A benchmark of ocular disease intelligent recognition: One shot for multi-disease detection. Lecture Notes in Computer Science (including Subseries Lecture Notes Artificial Intelligence, Lecture Notes Bioinformatics), 12614 LNCS, pp. 177–193 (2021). https://doi.org/10.1007/978-3-030-71058-3_11

  29. L.P. Cen, J. Ji, J.W. Lin, S.T. Ju, H.J. Lin, T.P. Li, Y. Wang, J.F. Yang, Y.F. Liu, S. Tan, L. Tan, D. Li, Y. Wang, D. Zheng, Y. Xiong, H. Wu, J. Jiang, Z. Wu, D. Huang, T. Shi T et al., Automatic detection of 39 FUNDUS diseases and conditions in retinal photographs using deep neural networks. Nat. Commun. 12 (1), 4828. https://doi.org/10.1038/s41467-021-25138-w

  30. A. Bali, V. Mansotra, Transfer learning-based one versus rest classifier for multiclass MultiLabel ophthalmological disease prediction. Int. J. Adv. Comput. Sci. Appl. (IJACSA), 12 (12), 537 546 (2021). https://doi.org/10.14569/IJACSA.2021.0121269

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, Jammu, 180006, India

    Akanksha Bali & Vibhakar Mansotra

Authors
  1. Akanksha Bali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vibhakar Mansotra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akanksha Bali .

Editor information

Editors and Affiliations

  1. IIS (Deemed to be University), Jaipur, Rajastan, India

    Vijay Singh Rathore

  2. Electronics and Computer Discipline, Indian Institute of Technology, Roorkee, Uttarakhand, India

    Subhash Chander Sharma

  3. Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    Joao Manuel R.S. Tavares

  4. Faculty of Science, School Information Systems, Queensland University of Technology, Brisbane, QLD, Australia

    Catarina Moreira

  5. Deparment of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal, Pondicherry, India

    B. Surendiran

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bali, A., Mansotra, V. (2022). FUNDUS and OCT Image Classification Using DL Techniques. In: Rathore, V.S., Sharma, S.C., Tavares, J.M.R., Moreira, C., Surendiran, B. (eds) Rising Threats in Expert Applications and Solutions. Lecture Notes in Networks and Systems, vol 434. Springer, Singapore. https://doi.org/10.1007/978-981-19-1122-4_8

Download citation

Publish with us

Policies and ethics

Search

Navigation