Skip to main content
SpringerLink
Log in

Early Diagnosis of Age-Related Macular Degeneration (ARMD) Using Deep Learning

  • Conference paper
  • First Online:
Intelligent Systems and Sustainable Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 289))

Abstract

Retinal diseases become more complicated for the humans. Among this, age-related macular degeneration (ARMD) is the eye-related disease that may cause vision loss. ARMD consists of two types such as dry-ARMD and wet ARMD. The dry ARMD gets affected slowly, so there is no vision loss. The wet ARMD shows the impact on vision loss. If the person got infected for two eyes, they may loss their quality of life. The dry form of age-related macular degeneration tends to get worse slowly, so you can keep most of your vision. The wet form of macular degeneration is a leading cause of permanent vision loss. If it is in both eyes, it can hurt your quality of life. Early detection of ARMD can prevent the vision loss for elderly persons. Deep learning (DL) is an artificial intelligence (AI) technique that works better on human body parts by generating patterns for decision-making. In this paper, discussed several preprocessing techniques, feature extraction techniques and early diagnosis of ARMD diseases by using deep learning algorithms. The performance of various algorithms is discussed on optical coherence tomography (OCT) dry and wet images.

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
Hardcover Book
USD 299.99
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. Krause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G.S., Peng, L., Webster, D.R.: Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology 125(8), 1264–1272 (2018)

    Article  Google Scholar 

  2. Das, V., Dandapat, S., Bora, P.K.: Multi-scale deep feature fusion for automated classification of macular pathologies from OCT images. Biomed. Signal Process. Control 54, 101605 (2019)

    Google Scholar 

  3. Hwang, D.-K., Hsu, C.-C., Chang, K.-J., Chao, D., Sun, C.-H., Jheng, Y.-C., Yarmishyn, A.A., Wu, J.-C., Tsai, C.-Y., Wang, M.-L., et al.: Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics 9(1), 232 (2019)

    Article  Google Scholar 

  4. Kaymak, S., Serener, A.: Automated age-related macular degeneration and diabetic macular edema detection on oct images using deep learning. In: 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE, pp. 265–269 (2018)

    Google Scholar 

  5. Lee, C.S., Baughman, D.M., Lee, A.Y.: Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Ophthalmol. Retina 1(4), 322–327 (2017)

    Article  Google Scholar 

  6. Sun, Y., Li, S., Sun, Z.: Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning. J. Biomed. Optics 22(1), 016012 (2017)

    Google Scholar 

  7. Sidibe, D., Sankar, S., Lemaitre, G., Rastgoo, M., Massich, J., Cheung, C.Y., Tan, G.S.W., Milea, D., Lamoureux, E., Wong, T.Y., et al.: An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images. Comput. Methods Programs Biomed. 139, 109–117 (2017)

    Article  Google Scholar 

  8. Perdomo, O., Rios, H., Rodríguez, F., Otálora, S., Meriaudeau, F., Müller, H., González, F.A.: Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. Comput. Methods Programs Biomed. (2019)

    Google Scholar 

  9. Awais, M., Müller, H., Tang, T.B., Meriaudeau, F.: Classification of sd-oct images using a deep learning approach. In: 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), IEEE, pp. 489–492 (2017)

    Google Scholar 

  10. Rasti, R., Rabbani, H., Mehridehnavi, A., Hajizadeh, F.: Macular OCT classification using a multi-scale convolutional neural network ensemble. IEEE Trans. Med. Imaging 37(4), 1024–1034 (2017)

    Article  Google Scholar 

  11. Seebock, P., Waldstein, S.M., Klimscha, S., Bogunovic, H., Schlegl, T., Gerendas, B.S., Donner, R., Schmidt-Erfurth, U., Langs, G.: Unsupervised identification of disease marker candidates in retinal OCT imaging data. IEEE Trans. Med. Imaging 38(4), 1037–1047 (2018)

    Article  Google Scholar 

  12. Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C.S., Liang, H., Baxter, S.L., McKeown, A., Yang, G., Wu, X., Yan, F., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)

    Article  Google Scholar 

  13. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  14. Huang, L., He, X., Fang, L., Rabbani, H., Chen, X.: Automatic classification of retinal optical coherence tomography images with layer guided convolutional neural network. IEEE Signal Process. Lett. 26(7), 1026–1030 (2019)

    Article  Google Scholar 

  15. Serener, A., Serte, S.: Dry and wet age-related macular degeneration classification using oct images and deep learning. In: 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), IEEE, pp. 1–4 (2019)

    Google Scholar 

  16. Hijazi, M., Coenen, F., Zheng, Y.: Retinal image classification using histogram based approach. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. Barcelona (2010)

    Google Scholar 

  17. Mookiah, M., Acharya, U., Koh, J., Chua, C.K.: Decision support system for age-related macular degeneration using discrete wavelet transform. Med. Biol. Eng. Comput. 52, 781–796 (2014)

    Google Scholar 

  18. Mookiah, M.R.K., Acharya, U., Koh, J., Chandran, V.: Automated diagnosis of age-related macular degeneration using greyscale features from digital fundus images. Comput. Biol. Med. 53, 55–64 (2014)

    Article  Google Scholar 

  19. Zheng, Y., Hijazi, M., Coenen, F.: Automated disease/no disease grading of age-related macular degeneration by an image mining approach. Invest. Ophthalmol. Vis. Sci. 53(13), 8310–8318 (2012)

    Google Scholar 

  20. Acharya, U., Hagiwara, Y., Koh, J., Salatha: Automated screening tool for dry and wet age-related macular degeneration (ARMD) using pyramid of histogram of orientated gradients (PHOG) and nonlinear features. Comput. Sci. 20, 41–51 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India

    Pamula Udayaraju

  2. Department of Information Technology, Sathyabama Institute of Science and Technology, Chennai, India

    P. Jeyanthi

Authors
  1. Pamula Udayaraju
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Jeyanthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Malla Reddy University, Hyderabad, India

    V. Sivakumar Reddy

  2. Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, India

    V. Kamakshi Prasad

  3. Federation University at IIBIT, Sydney, NSW, Australia

    D. N. Mallikarjuna Rao

  4. KIIT University, Bhubaneswar, India

    Suresh Chandra Satapathy

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

Udayaraju, P., Jeyanthi, P. (2022). Early Diagnosis of Age-Related Macular Degeneration (ARMD) Using Deep Learning. In: Reddy, V.S., Prasad, V.K., Mallikarjuna Rao, D.N., Satapathy, S.C. (eds) Intelligent Systems and Sustainable Computing. Smart Innovation, Systems and Technologies, vol 289. Springer, Singapore. https://doi.org/10.1007/978-981-19-0011-2_59

Download citation

Publish with us

Policies and ethics

Search

Navigation