Skip to main content

Advertisement

SpringerLink
Log in

Classification of diabetic macular edema severity using deep learning technique

  • Original Article
  • Published:
Research on Biomedical Engineering Aims and scope Submit manuscript

Abstract

Purpose

Diabetic macular edema (DME) is a kind of hard exudates lesion seen near the diabetic macular region of the retina. DME causes visual loss and may result in complete blindness; early identification and treatment may be able to cure this. Identification of DME at an early stage is a challenging and error-prone task. To address this issue, the article presents a methodology that uses the notion of transfer learning to identify cases of DME from retinal fundus images.

Methods

A pre-trained DenseNet121 is used in this technique to extract the useful set of feature vectors from the fundus images, which are then fed into a few additional fully connected layers and then into the classification layer to classify DME instances. A total of 577 fundus training images from 3 DME classes were used to train the proposed model, and 103 fundus testing images were used to verify the proposed model for classifying them into one of the three DME cases.

Results

The suggested model is trained and tested on the Indian Diabetic Retinopathy Image Dataset (IDRiD). With the test images, the results demonstrate that the proposed model outperformed the state-of-the-art models presented in “Diabetic Retinopathy – Segmentation and Grading Challenge” held at ISBI-2018 with an accuracy of 86.4%.

Conclusion

The proposed model diagnoses DME at an early stage for timely treatment and helps to reduce the workload of ophthalmologists.

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

Similar content being viewed by others

Data availability

The dataset (Indian Diabetic Retinopathy Image Dataset (IDRiD)) is publicly available at https://www.kaggle.com/aaryapatel98/indian-diabetic-retinopathy-image-dataset.

Code availability

The developed code is available at https://github.com/amitbsbkumar/amitbsbkumar.

References

  • Acharya UR, Mookiah MR, Koh JE, Tan JH, Bhandary SV, Rao AK, Hagiwara Y, Chua CK, Laude A. Automated diabetic macular edema (DME) grading system using DWT, DCT features and maculopathy index. Comput Biol Med. 2017;1(84):59–68.

    Article  Google Scholar 

  • Akram MU, Tariq A, Khan SA, Javed MY. Automated detection of exudates and macula for grading of diabetic macular edema. Comput Methods Programs Biomed. 2014;114(2):141–52.

    Article  Google Scholar 

  • Al-Bander B, Al-Nuaimy W, Al-Taee MA, Williams BM, Zheng Y. (n.d) Diabetic macular edema grading based on deep neural networks.

  • Bandello F, Lanzetta P, Loewenstein A, Massin P, Menchini F, Veritti D. Diabetic macular edema. Dev Ophthalmol. 2017;28(58):102–38.

    Article  Google Scholar 

  • Budai A, Odstrcilik J, Kolar R, Hornegger J, Jan J, Kubena T, Michelson G. A public database for the evaluation of fundus image segmentation algorithms. Invest Ophthalmol Visual Sci. 2011;52(14):1345.

    Google Scholar 

  • Carin L, Pencina MJ. On deep learning for medical image analysis. JAMA. 2018;320(11):1192–3.

    Article  Google Scholar 

  • Ciulla TA, Amador AG, Zinman B. Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies. Diabetes Care. 2003;26(9):2653–64.

    Article  Google Scholar 

  • Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A, Charton B. Feedback on a publicly distributed image database: the Messidor database. Image Anal Stereol. 2014;33(3):231–4.

    Article  Google Scholar 

  • Dutta A, Agarwal P, Mittal A, Khandelwal S. Detecting grades of diabetic retinopathy by extraction of retinal lesions using digital fundus images. Res Biomed Eng. 2021;9:1–6.

    Google Scholar 

  • Fong DS, Aiello L, Gardner TW, King GL, Blankenship G, Cavallerano JD, Ferris FL, Klein R. Retinopathy in diabetes. Diabetes Care. 2004;27(suppl 1):s84–7.

    Article  Google Scholar 

  • Giancardo L, Meriaudeau F, Karnowski TP, Tobin KW, Grisan E, Favaro P, Ruggeri A, Chaum E. Textureless macula swelling detection with multiple retinal fundus images. IEEE Trans Biomed Eng. 2010;58(3):795–9.

    Article  Google Scholar 

  • Giancardo L, Meriaudeau F, Karnowski TP, Li Y, Tobin KW, Chaum E Automatic retina exudates segmentation without a manually labelled training set. In 2011 IEEE Int Symp Biomed Imaging From Nano to Macro 2011 (1396–1400) IEEE

  • Giancardo L. The Hamilton Eye Institute Macular Edema dataset (HEI-MED). GitHub https://github. com/lgiancaUTH/HEI-MED. 2012

  • Goldbaum M. The STARE Project, Structured Analysis of the Retina Database. Zuletzt abgerufen am 27

  • He J, Shen L, Ai X, et al. Diabetic retinopathy grade and macular edema risk classification using convolutional neural networks. In: 2019 IEEE Int Conference on Power, Intelligent Computing Syst (ICPICS) IEEE 2019 463-466

  • 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 2016:770–778

  • Hee MR, Puliafito CA, Wong C, Duker JS, Reichel E, Rutledge B, Schuman JS, Swanson EA, Fujimoto JG. Quantitative assessment of macular edema with optical coherence tomography. Arch Ophthalmol. 1995;113(8):1019–29.

    Article  Google Scholar 

  • Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition 2017:4700–4708

  • Iandola F, Moskewicz M, Karayev S, Girshick R, Darrell T, Keutzer K. Densenet: implementing efficient convnet descriptor pyramids. arXiv preprint http://arxiv.org/abs/1404.1869

  • Kandhasamy JP, Balamurali S, Kadry S, Ramasamy LK. Diagnosis of diabetic retinopathy using multi level set segmentation algorithm with feature extraction using SVM with selective features. Multimedia Tools App. 2020;79(15):10581–96.

    Article  Google Scholar 

  • Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Pietila J, Kalviainen H, Uusitalo H. DIARETDB1-standard diabetic retino-pathy database. IMAGERET-Optimal Detect Decis Diagnosis Diabet Retin 2007:15

  • Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25:1097–105.

    Google Scholar 

  • Math L, Fatima R. Adaptive machine learning classification for diabetic retinopathy. Multimedia Tools App. 2021;80(4):5173–86.

    Article  Google Scholar 

  • de Moura J, Novo J, Ortega M. Deep feature analysis in a transfer learning-based approach for the automatic identification of diabetic macular edema. In2019 Int Joint Conference Neural Net (IJCNN) 2019:1–8 IEEE

  • Murugan R, Roy P, Singh U. An abnormality detection of retinal fundus images by deep convolutional neural networks. Multimedia Tools App. 2020;79(33):24949–67.

    Article  Google Scholar 

  • Nair LR. RetoNet: a deep learning architecture for automated retinal ailment detection. Multimedia Tools App. 2020;79(21):15319–28.

    Article  Google Scholar 

  • Niemeijer M, Staal J, van Ginneken B, Loog M, Abramoff MD. Comparative study of retinal vessel segmentation methods on a new publicly available database. In Medical imaging 2004: image processing. Int Soc Optics Photonics. 2004;5370:648–56.

    Google Scholar 

  • Patil SB, Patil BP. Automated macula proximity diagnosis for early finding of diabetic macular edema. Res Biomed Eng. 2020;36:249–65.

    Article  Google Scholar 

  • Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F. Indian Diabetic Retinopathy Image Dataset (IDRiD): a database for diabetic retinopathy screening research. Data. 2018;3(3):25.

    Article  Google Scholar 

  • Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, Liu L, Wang J, Liu X, Gao L, Wu T. Idrid: Diabetic retinopathy–segmentation and grading challenge. Med Image Anal. 2020;1(59):101561.

    Article  Google Scholar 

  • Raja H, Akram MU, Khawaja SG, Arslan M, Ramzan A, Nazir N. Data on OCT and fundus images for the detection of glaucoma. Data Brief. 2020;1(29):105342.

    Article  Google Scholar 

  • Ren F, Cao P, Zhao D, Wan C. Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning. Technol Health Care. 2018;26(S1):389–97.

    Article  Google Scholar 

  • da Rocha DA, Barbosa AB, Guimarães DS, Gregório LM, Gomes LH, da Silva AL, Peixoto ZM. An unsupervised approach to improve contrast and segmentation of blood vessels in retinal images using CLAHE, 2D Gabor wavelet, and morphological operations. Res Biomed Eng. 2020;36(1):67–75.

    Article  Google Scholar 

  • Saman G, Gohar N, Noor S, Shahnaz A, Idress S, Jehan N, Rashid R, Khattak SS. Automatic detection and severity classification of diabetic retinopathy. Multimedia Tools App. 2020;79(43):31803–17.

    Article  Google Scholar 

  • dos Santos JC, Carrijo GA, dos Santos Cardoso CD, Ferreira JC, Sousa PM, Patrocínio AC. Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter. Res Biomed Eng. 2020;11:1–3.

    Google Scholar 

  • Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35(5):1285–98.

    Article  Google Scholar 

  • da Silva AL, Ferreira FM, Guimarães JR, Peixoto ZM. Automatic segmentation of blood vessels in retinal images using 2D Gabor wavelet and sub-image thresholding resulting from image partition. Res Biomed Eng. 2019;35(3):241–9.

    Google Scholar 

  • Simonyan K, Zisserman A. 2014 Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  • Syed AM, Akram MU, Akram T, Muzammal M, Khalid S, Khan MA. Fundus images-based detection and grading of macular edema using robust macula localization. IEEE Access. 2018;15(6):58784–93.

    Article  Google Scholar 

  • Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition 2015:1–9

  • Yu F, Wang D, Shelhamer E, Darrell T. Deep layer aggregation. In Proceedings of the IEEE conference on computer vision and pattern recognition 2018:2403–2412

  • Zhou W, Wu C, Chen D, Yi Y, Du W. Automatic microaneurysm detection using the sparse principal component analysis-based unsupervised classification method. IEEE Access. 2017;20(5):2563–72.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, National Institute of Technology Patna, Patna, India

    Amit Kumar, Anand Shanker Tewari & Jyoti Prakash Singh

Authors
  1. Amit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anand Shanker Tewari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jyoti Prakash Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The first author (Amit Kumar) developed the research objective in consultation with the second author, implemented it, and evaluated the results. He also developed the first draft of the article.

The second author (Anand Shanker Tewari) helped in experiments and corrected the initial draft.

The third author (Jyoti Prakash Singh) helped in shaping the research objectives, correcting, and finalizing the article.

Corresponding author

Correspondence to Amit Kumar.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare no competing interests.

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 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

Kumar, A., Tewari, A.S. & Singh, J.P. Classification of diabetic macular edema severity using deep learning technique. Res. Biomed. Eng. 38, 977–987 (2022). https://doi.org/10.1007/s42600-022-00233-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42600-022-00233-z

Keywords

Search

Navigation