Skip to main content

Advertisement

SpringerLink
Log in

Self-supervised category selective attention classifier network for diabetic macular edema classification

  • Original Article
  • Published:
Acta Diabetologica Aims and scope Submit manuscript

Abstract

Aims

This study aims to develop an advanced model for the classification of Diabetic Macular Edema (DME) using deep learning techniques. Specifically, the objective is to introduce a novel architecture, SSCSAC-Net, that leverages self-supervised learning and category-selective attention mechanisms to improve the precision of DME classification.

Methods

The proposed SSCSAC-Net integrates self-supervised learning to effectively utilize unlabeled data for learning robust features related to DME. Additionally, it incorporates a category-specific attention mechanism and a domain-specific layer into the ResNet-152 base architecture. The model is trained using an ensemble of unsupervised and supervised learning techniques. Benchmark datasets are utilized for testing the model's performance, ensuring its robustness and generalizability across different data distributions.

Results

Evaluation of the SSCSAC-Net on multiple datasets demonstrates its superior performance compared to existing techniques. The model achieves high accuracy, precision, and recall rates, with an accuracy of 98.7%, precision of 98.6%, and recall of 98.8%. Furthermore, the incorporation of self-supervised learning reduces the dependency on extensive labeled data, making the solution more scalable and cost-effective.

Conclusions

The proposed SSCSAC-Net represents a significant advancement in automated DME classification. By effectively using self-supervised learning and attention mechanisms, the model offers improved accuracy in identifying DME-related features within retinal images. Its robustness and generalizability across different datasets highlight its potential for clinical applications, providing a valuable tool for clinicians in diagnosing DME effectively.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Sachin Chavan, Nitin Choubey. The first draft of the manuscript was written by Sachin Chavan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

References

  1. Iyer SS, Radhakrishnan NS, Roohipourmoallai R, Guerin CM, Maylath JS, Garson N (2023) Chronic ocular small vessel disease: An overview of diabetic retinopathy and its relationship with cardiovascular health. American Heart Journal Plus: Cardiology Research and Practice. 100270.

  2. Semeraro F, Morescalchi F, Cancarini A, Russo A, Rezzola S, Costagliola C (2019) Diabetic retinopathy, a vascular and inflammatory disease: therapeutic implications. Diabetes Metab 45(6):517–527

    Article  CAS  PubMed  Google Scholar 

  3. Lanzetta P, Sarao V, Scanlon PH, Barratt J, Porta M, Bandello F, Loewenstein A (2020) Fundamental principles of an effective diabetic retinopathy screening program. Acta Diabetol 57:785–798

    Article  PubMed  PubMed Central  Google Scholar 

  4. Liu X, Ali TK, Singh P, Shah A, McKinney SM, Ruamviboonsuk P, Turner AW, Keane PA, Chotcomwongse P, Nganthavee V, Chia M (2022) Deep learning to detect OCT-derived diabetic macular edema from color retinal photographs: a multicenter validation study. Ophthalmol Retina 6(5):398–410

    Article  PubMed  Google Scholar 

  5. Li X, Hu X, Yu L, Zhu L, Fu CW, Heng PA (2019) CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans Med Imaging 39(5):1483–1493

    Article  PubMed  Google Scholar 

  6. Chalakkal R, Hafiz F, Abdulla W, Swain A (2021) An efficient framework for automated screening of Clinically Significant Macular Edema. Comput Biol Med 130:104128

    Article  PubMed  Google Scholar 

  7. Chalakkal RJ, Abdulla WH, Hong SC (2020) Fundus retinal image analyses for screening and diagnosing diabetic retinopathy, macular edema, and glaucoma disorders. InDiabetes and Fundus OCT (pp 59–111). Elsevier

  8. Lalithadevi B, Krishnaveni S (2022) Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: a comprehensive review. Concurr Comput Pract Exp 34(19):e7032

    Article  Google Scholar 

  9. Mathews MR, Anzar SM (2021) A comprehensive review on automated systems for severity grading of diabetic retinopathy and macular edema. Int J Imaging Syst Technol 31(4):2093–2122

    Article  Google Scholar 

  10. Sundaram S, Selvamani M, Raju SK, Ramaswamy S, Islam S, Cha JH, Almujally NA, Elaraby A (2023) Diabetic retinopathy and diabetic macular edema detection using ensemble based convolutional neural networks. Diagnostics 13(5):1001

    Article  PubMed  PubMed Central  Google Scholar 

  11. Senthamizh Selvi R, Bragadesh Bharatwaj S, Ajith Kumar B, Bharath Raj VR, Sudha S (2021) Convolutional neural network-based detection and classification of cardiovascular disease and diabetic macular edema. InMicro-Electronics and telecommunication engineering: proceedings of 4th ICMETE 2020 (pp. 407–422). Springer Singapore

  12. Everett LA, Paulus YM (2021) Laser therapy in the treatment of diabetic retinopathy and diabetic macular edema. Curr DiabRep 21:1–2

    Google Scholar 

  13. Brito P, Costa J, Gomes N, Costa S, Correia-Pinto J, Silva R (2018) Serological inflammatory factors as biomarkers for anatomic response in diabetic macular edema treated with anti-VEGF. J Diabetes Complicat 32(7):643–649

    Article  Google Scholar 

  14. Cacciamani A, Esposito G, Scarinci F, Parravano M, Dinice L, Di Nicola M, Micera A (2019) Inflammatory mediators in the vitreal reflux of patients with diabetic macular edema. Graefes Arch Clin Exp Ophthalmol 257:187–197

    Article  CAS  PubMed  Google Scholar 

  15. Islam MR, Abdulrazak LF, Nahiduzzaman M, Goni MO, Anower MS, Ahsan M, Haider J, Kowalski M (2022) Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images. Comput Biol Med 146:105602

    Article  PubMed  Google Scholar 

  16. Bhardwaj C, Jain S, Sood M (2021) Deep learning–based diabetic retinopathy severity grading system employing quadrant ensemble model. J Digit Imaging 34:440–457

    Article  PubMed  PubMed Central  Google Scholar 

  17. Yang Y, Shang F, Wu B, Yang D, Wang L, Xu Y, Zhang W, Zhang T (2021) Robust collaborative learning of patch-level and image-level annotations for diabetic retinopathy grading from fundus image. IEEE Trans Cybern 52(11):11407–11417

    Article  Google Scholar 

  18. Chavan S, Choubey N (2023) An automated diabetic retinopathy of severity grade classification using transfer learning and fine-tuning for fundus images. Multimed Tools Appl 82(24):36859–36884

    Article  Google Scholar 

  19. Alahmadi MD (2022) Texture attention network for diabetic retinopathy classification. IEEE Access 10:55522–55532

    Article  Google Scholar 

  20. Farag MM, Fouad M, Abdel-Hamid AT (2022) Automatic severity classification of diabetic retinopathy based on denseNet and convolutional block attention module. IEEE Access 10:38299–38308

    Article  Google Scholar 

  21. Bilal A, Sun G, Li Y, Mazhar S, Khan AQ (2021) Diabetic retinopathy detection and classification using mixed models for a disease grading database. IEEE Access 9:23544–23553

    Article  Google Scholar 

  22. Al-Antary MT, Arafa Y (2021) Multi-scale attention network for diabetic retinopathy classification. IEEE Access 9:54190–54200

    Article  Google Scholar 

  23. Wu T, Liu L, Zhang T, Wu X (2022) Deep learning-based risk classification and auxiliary diagnosis of macular edema. Intell Based Med 6:100053

    Article  Google Scholar 

  24. Wu J, Zhang Q, Liu M, Xiao Z, Zhang F, Geng L, Liu Y, Wang W (2021) Diabetic macular edema grading based on improved Faster R-CNN and MD-ResNet. Signal Image Video Process 15:743–751

    Article  Google Scholar 

  25. Altan G (2022) DeepOCT: an explainable deep learning architecture to analyze macular edema on OCT images. Eng Sci Technol Int J 34:101091

    Google Scholar 

  26. Saini DJ, Sivakami R, Venkatesh R, Raghava CS, Dwarkanath PS, Anwer TM, Smirani LK, Ahammad SH, Pamula U, Hossain MA, Rashed AN (2023) Convolution neural network model for predicting various lesion-based diseases in diabetic macula edema in optical coherence tomography images. Biomed Signal Process Control 86:105180

    Article  Google Scholar 

  27. Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Anal Appl 24:1207–1220

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

There is no funding for this study.

Author information

Authors and Affiliations

  1. SVKM’S NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India

    Sachin Chavan & Nitin Choubey

Authors
  1. Sachin Chavan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nitin Choubey
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SC contributed to conceptualization, methodology, writing—original draft preparation, writing—review and editing. SC and NC contributed to formal analysis and investigation. NC supervised the study. All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sachin Chavan.

Ethics declarations

Conflict of interest

Authors declares that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants and/or animals performed by any of the authors.

Informed consent

There is no informed consent for this study.

Additional information

Managed By Massimo Federici.

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chavan, S., Choubey, N. Self-supervised category selective attention classifier network for diabetic macular edema classification. Acta Diabetol (2024). https://doi.org/10.1007/s00592-024-02257-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00592-024-02257-6

Keywords

Search

Navigation