[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

DR-Net: Diabetic Retinopathy detection with fusion multi-lesion segmentation and classification

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

Abstract

Diabetic Retinopathy (DR) is one of the most common microvascular complications of diabetes mellitus and is a major cause of blurred vision, vision loss, and blindness. Depending on the severity of the disease, DR is divided into non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). Current research has focused on using Deep Learning (DL) models to classify fundus images based on DR severity. To make the lesions in DR images more visible and to make DR detection easier, this study proposes a two-phase classification model (DR-Net). SR-Net (SE-Block-ResNet) is the first phase of the network in this study, the second phase consists of MT-SNet (Multiple lesions-TransUnet-Segmentation-Net) and SRVGG (SE-Block-RepVGG). The first phase uses ST-Net to classify NPDR images with PDR images, while the second phase first implements segmentation of multiple lesions, followed by classification of the processed NPDR images. The accuracy on the DDR dataset is improved by 2.21% compared to the new study.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

Data availability

The datasets used in the paper are from previously reported studies and datasets that have been cited.

References

  1. Bodapati JD, Shaik NS, Naralasetti V (2021) Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J Ambient Intell Humaniz Comput 12(10):9825–9839. https://doi.org/10.1007/s12652-020-02727-z

    Article  Google Scholar 

  2. Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2021) Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:2105.05537. https://doi.org/10.48550/arXiv.2105.05537

  3. Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou, Y (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306. https://doi.org/10.48550/arXiv.2102.04306

  4. Das S, Kharbanda K, Suchetha M, Raman R, Dhas E (2021) Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed Signal Process Control 68:102600. https://doi.org/10.1016/j.bspc.2021.102600

    Article  Google Scholar 

  5. Developers S-L (2021) Metrics and scoring: Quantifying the quality of predictions. User Guide,[entre 2007 e 2019]. Dispon’ıvel em: https://scikit-learn.org/stable/modules/modelevaluation.html. Acesso em 26

  6. Ding X, Zhang X, Ma N, Han J, Ding G, Sun J (2021) Repvgg: Making vgg-style convnets great again. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13733–13742. https://doi.org/10.48550/arXiv.2101.03697

  7. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al. (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. https://doi.org/10.48550/arXiv.2010.11929

  8. Elwin JGR, Mandala J, Maram B, Kumar RR (2022) Ar-hgso:autoregressive-henry gas sailfish optimization enabled deep learning model for diabetic retinopathy detection and severity level classification. Biomed Signal Process Control 77:103712. https://doi.org/10.1016/j.bspc.2022.103712

    Article  Google Scholar 

  9. Foo A, Hsu W, Lee ML, Lim G, Wong TY (2020) Multi-task learning fordiabetic retinopathy grading and lesi on segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence 34:13267–13272. https://doi.org/10.1609/aaai.v34i08.7035

    Article  Google Scholar 

  10. Gangwar AK, Ravi V (2021) Diabetic retinopathy detection using transfer learning and deep learning. In: Evolution in Computational Intelligence, pp. 679–689. Springer, ??? . https://doi.org/10.1007/978-981-15-5788-064

  11. Garifullin A, Lensu L, Uusitalo H (2021) Deep bayesian baseline for segmenting diabetic retinopathy lesions: advances and challenges. Comput Biol Med 136:104725. https://doi.org/10.1016/j.compbiomed.2021.104725

    Article  Google Scholar 

  12. Guo Y, Peng Y (2022) Multiple lesion segmentation in diabetic retinopathy with dual-input attentive refinenet. Appl Intell, 1–25. https://doi.org/10.1007/s10489-022-03204-0

  13. Hemanth DJ, Deperlioglu O, Kose U (2020) An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput & Applic 32(3):707–721. https://doi.org/10.1007/s00521-018-03974-0

    Article  Google Scholar 

  14. Huang S, Li J, Xiao Y, Shen N, Xu T (2022) Rtnet: relation transformer network for diabetic retinopathy multi-lesion segmentation. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2022.3143833

  15. Kalyani G, Janakiramaiah B, Karuna A, Prasad L (2021) Diabetic retinopathy detection and classification using capsule networks. Complex Intell Syst, 1–14

  16. Karim AM, Güzel MS, Tolun MR, Kaya H, Celebi FV (2019) A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing. Biocybernetics Biomed Eng 39(1):148–159. https://doi.org/10.1016/j.bbe.2018.11.004

    Article  Google Scholar 

  17. Kumar A, Megha PM, Meenakshy K (2020) Diabetic retinopathy detection & classification techniques: a review. Int J Sci Tech Res 9:1621–1628

    Google Scholar 

  18. Li T, Gao Y, Wang K, Guo S, Liu H, Kang H (2019) Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Inf Sci 501:511–522. https://doi.org/10.1016/j.ins.2019.06.011

    Article  Google Scholar 

  19. Math L, Fatima R (2021) Adaptive machine learning classification for diabetic retinopathy. Multimed Tools Appl 80(4):5173–5186. https://doi.org/10.1007/s11042-020-09793-7

    Article  Google Scholar 

  20. Mo J, Zhang L, Feng Y (2018) Exudate-based diabetic macular edemarecognition in retinal images using casc aded deep residual networks. Neurocomputing 290:161–171. https://doi.org/10.1016/j.neucom.2018.02.035

    Article  Google Scholar 

  21. Panwar A, Semwal G, Goel S, Gupta S (2022) Stratification of the lesions in color fundus images of diabetic retinopathy patients using deep learning models and machine learning classifiers. In: Edge Analytics, pp. 653–666. Springer. https://doi.org/10.1007/978-981-19-0019-849

  22. Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, Liu L, Wang J, Liu X, Gao L et al (2020) Idrid: Diabetic retinopathy segmentation and grading challenge. Med Image Anal 59:101561. https://doi.org/10.1016/j.media.2019.101561

    Article  Google Scholar 

  23. Prasanna P, Samiksha P, Ravi K, Manesh K, Girish D, Vivek S, Fabrice M (2018) Indian diabetic retinopathy image dataset (idrid). IEEE Dataport. https://doi.org/10.3390/data3030025

  24. Qureshi I, Ma J, Abbas Q (2021) Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning. Multimed Tools Appl 80(8):11691–11721. https://doi.org/10.1007/s11042-020-10238-4

    Article  Google Scholar 

  25. Rayavel P, Murukesh C (2022) A novel approach for identification of biomakers in diabetic retinopathy recognition. J Med Imaging Health Inform 12(2):149–154. https://doi.org/10.1166/jmihi.2022.3934

    Article  Google Scholar 

  26. Sambyal N, Saini P, Syal R, Gupta V (2020) Modified u-net architecture for semantic segmentation of diabetic retinopathy images. Biocybern Biomed Eng 40(3):1094–1109

    Article  Google Scholar 

  27. Sivari E, Güzel MS, Bostanci E, Mishra A (2022) A novel hybrid machine learning based system to classify shoulder implant manufacturers. In: Healthcare 10:580. https://doi.org/10.3390/healthcare10030580

    Article  Google Scholar 

  28. Sungheetha A, Sharma R (2021) Design an early detection and classification for diabetic retinopathy by deep feature extraction based convolution neural network. J Trends Comput Sci Smart Technol (TCSST) 3(02):81–94. https://doi.org/10.36548/jtcsst.2021.2.002

    Article  Google Scholar 

  29. Tan JH, Fujita H, Sivaprasad S, Bhandary SV, Rao AK, Chua KC, Acharya UR (2017) Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf Sci 420:66–76. https://doi.org/10.1016/j.ins.2017.08.050

    Article  Google Scholar 

  30. Thanh DN, Sergey D, Surya Prasath V, Hai NH (2019) Blood vessels segmentation method for retinal fundus images based on adaptive principal curvature and image derivative operators. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences XLII-2/W12:211–218. https://doi.org/10.5194/isprs-archives-XLII-2-W12-211-2019

    Article  Google Scholar 

  31. Thiagarajan AS, Adikesavan J, Balachandran S (2020) Ramamoorthy, B.G.:diabetic retinopathy detection using deep learning techniques. J Comput Sci 16:305–313

    Article  Google Scholar 

  32. Tuyet VTH, Binh NT, Tin DT (2022) Improving the curvelet saliency and deep convolutional neural networks for diabetic retinopathy classification in fundus images. Eng Technol Appl Sci Res 12(1):8204–8209. https://doi.org/10.48084/etasr.4679

    Article  Google Scholar 

  33. Wang L, Chen Z, Wang M, Wang T, Zhu W, Chen X (2021) Cycle adaptive multi-target weighting network for automated diabetic retinopathy segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1141–1144. https://doi.org/10.1109/ISBI48211.2021.9433917

  34. Xu Y, Zhou Z, Li X, Zhang N, Zhang M, Wei P (2021) Ffu-net:Feature fusion u-net for lesion segmentation of diabetic retinopathy. BioMed Res Int 2021. https://doi.org/10.1155/2021/6644071

  35. Yerushalmy J (1947) Statistical problems in assessing methods of medical diagnosis, with special reference to x-ray techniques. Public Health Reports (1896–1970), 1432–1449. https://doi.org/10.2307/4586294

  36. Zapata MA, Royo-Fibla D, Font O, Vela JI, Marcantonio I, Moya-Sánchez EU, Sánchez-Pérez A, Garcia-Gasulla D, Cortés U, Ayguadé E et al (2020) Artificial intelligence to identify retinal fundus images,quality validation, laterality evaluation, macular degeneration, and suspected glaucoma. Clin Ophthalmol (Auckland, NZ) 14:419. https://doi.org/10.2147/OPTH.S235751

    Article  Google Scholar 

  37. Zhou Y, Wang B, Huang L, Cui S, Shao L (2020) A benchmark for studying diabetic retinopathy: segmentation, grading, and transferability. IEEE Trans Med Imaging 40(3):818–828. https://doi.org/10.1109/TMI.2020.3037771

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (2572021BH01) and the National Natural Science Foundation of China (62172087).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150040, China

    Yu Chen & Shibao Xu

  2. College of Mecheanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China

    Jun Long & Yining Xie

Authors
  1. Yu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shibao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yining Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yining Xie.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Additional information

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

Chen, Y., Xu, S., Long, J. et al. DR-Net: Diabetic Retinopathy detection with fusion multi-lesion segmentation and classification. Multimed Tools Appl 82, 26919–26935 (2023). https://doi.org/10.1007/s11042-023-14785-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14785-4

Keywords

Search

Navigation