Skip to main content
SpringerLink
Log in

Multi-input 2-dimensional deep belief network: diabetic retinopathy grading as case study

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The most important action in treating diabetic retinopathy is early diagnosis and its progression degree. This paper presents a two-dimensional Deep Belief Network based on Mixed-restricted Boltzmann Machine capable of receiving multiple two-dimensional inputs. Using multiple inputs provides more appropriate prior information for learning. In this proposed method, the image is transferred to the HSV color space and then the 3D color image is converted to a 2D matrix using a weighted mean. This weighted mean is calculated based on the entropy criterion. The resulting two-dimensional matrix is not in pixel and is merely a raw description of the image. The local, regional and global descriptions are extracted from this matrix and provided for the network. The proposed deep network automatically extracts the appropriate features to determine the progression degree of diabetic retinopathy by the network. Window by window image processing can overcome one of the basic problems of image classification, i.e. the small number of labeled data. Experiments showed that the proposed method is superior when compared to other methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Abramoff M, Niemeijer M, Suttorp-Schulten M, Viergever MA, RusSel RS, Van Ginneken B (2008) Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes. Diabetes Care

  2. Abramoff M, Reinhardt J, Russell S, Folk J, Mahajan V, Niemeijer M, Quellec G (2010) Automated Early Detection of Diabetic Retinopathy. Ophthalmol

  3. Agurto C, Barriga ES, Murray V, Nemeth S, Crammer R, Bauman W, Zamora G, Pattichis MS, Soliz P (2011) Automatic Detection of Diabetic Retinopathy and Age-Related Macular Degeneration in Digital Fundus Images. Investig Ophthalmol Vis Sci

  4. Agurto C, Barriga ES, Murray V, Nemeth S, Crammer R, Bauman W, Zamora G, Pattichis MS, Soliz P (2011) Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images. Investigative Ophthalmol Vis Sci

  5. Antal B, Hajdu A (2014) An ensemble-based system for automatic screening of diabetic retinopathy, Knowledge-Based Systems

  6. Arnold L, Rebecchi S, Chevallier S, Paugam-Moisy H (2011) An introduction to deep learning, ESANN

  7. Asiri N, Hussain M, Al Adel F, Alzaidi N (2019) Deep learning-based computer-aided diagnosis Systems for Diabetic Retinopathy: a survey, Artificial Intelligence in Medicine

  8. Bautista PA, Yagi Y (2010) Improving the visualization and detection of tissue folds in whole slide images through color enhancement. J Pathol Inform 1:25

    Article  Google Scholar 

  9. Bengio Y (2013) Deep learning of representations: looking forward. In: International Conference on Statistical Language and Speech Processing (SLSP)

  10. Chebbout S, Merouani HF (2012) Comparative study of clustering based color image segmentation techniques. International IEEE Conference on Signal Image Technology and Internet Based Systems (SITIS)

  11. De La Torre J, Valls A, Puig D (2019) A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing

  12. Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine Learning for Medical Imaging, Radiographics

  13. Gelman R (2019) Evaluation of Transfer Learning for Classification of:(1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography, arXiv preprint arXiv:1902.04151

  14. Grewal PS, Oloumi F, Rubin U, Tennant MT (2018) Deep learning in ophthalmology: a review. Can J Ophthalmol

  15. Gudla S, Tenneti D, Pande M, Tipparaju SM (2018) Diabetic retinopathy: pathogenesis, treatment and complications, Drug Delivery for The Retina and Posterior Segment Disease. Springer, Cham

    Google Scholar 

  16. He X, Zemel RS, Carreira-Perpiñán MA (2004) Multiscale conditional random fields for image labeling. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)

  17. Hinton GE, Osindero S, Teh Y (2006) A Fast Learning Algorithm for Deep Belief Nets. Neural Comput

  18. Ishida T, Hotta K (2015) Image labeling by integrating local, middle and global information. In: International IEEE Conference on Digital Image Computing: Techniques and Applications (DICTA)

  19. Kaggle: Diabetic Retinopathy Detection, https://www.kaggle.com/c/diabetic-retinopathy detection

  20. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing

  21. Messidor: http://www.adcis.net/en/third-party/messidor

  22. Mori T (2002) Information gain ratio as term weight: the case of summarization of IR results. International Conference on Computational Linguistics, Association for Computational Linguistics (ACL)

  23. NCSS Statistical Software Chapter (2004) One ROC Curve and Cutoff Analysis”, NCSS, LLC. All Rights Reserved (ncss.com)

  24. Nickfarjam AM, Ebrahimpour-Komleh H (2015) Multi-input topology of deep belief networks for image segmentation, International IEEE Congress on Technology, Communication and Knowledge (ICTCK)

  25. A. M. Nickfarjam, H. Ebrahimpour-Komleh (2019) Multi-input 1-dimensional deep belief network: action and activity recognition as case study. Multimedia Tools and Applications

  26. Islam SMS, Hasan MM, Abdullah S (2018) Deep Learning Based Early Detection and Grading of Diabetic Retinopathy using Retinal Fundus Images, Arxiv Preprint Arxiv:1812.10595

  27. Suzuki K (2017) Overview of deep learning in medical imaging. Radiol Phys Technol

  28. Wang W, Lo A (2018) Diabetic retinopathy: pathophysiology and treatments. Int J Mol Sci

  29. Wang Z, Yin Y, Shi J, Fang W, Li H, Wang X (2017) Zoom-in-net: deep mining lesions for diabetic retinopathy detection, international conference on medical image computing and computer assisted intervention (MICCAI)

  30. Yu D, Deng L (2011) Deep learning and its applications to signal and information processing. IEEE Signal Processing Magazine

  31. Zhao Z, Zhang K, Hao X, Tian J, Chua MCH, Chen L, Xu X (2019) Bira-Net: Bilinear Attention Net for Diabetic Retinopathy Grading, Arxiv Preprint Arxiv:1905.06312

  32. Zhou K, Gu Z, Liu W, Luo W, Cheng J, Gao S, Liu J (2018) Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Kashan, Kashan, Iran

    Amirali Amini Tehrani, Ali Mohammad Nickfarjam & Hossein Ebrahimpour-komleh

  2. School of Medicine, Kashan University of Medical Science, Kashan, Iran

    Dawood Aghadoost

Authors
  1. Amirali Amini Tehrani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Mohammad Nickfarjam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hossein Ebrahimpour-komleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dawood Aghadoost
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hossein Ebrahimpour-komleh.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tehrani, A.A., Nickfarjam, A.M., Ebrahimpour-komleh, H. et al. Multi-input 2-dimensional deep belief network: diabetic retinopathy grading as case study. Multimed Tools Appl 80, 6171–6186 (2021). https://doi.org/10.1007/s11042-020-10025-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10025-1

Keywords

Search

Navigation