Skip to main content
SpringerLink
Log in

RDD-Net: retinal disease diagnosis network: a computer-aided diagnosis technique using graph learning and feature descriptors

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Ocular diseases are a prevalent disease among the aging population across the world. The retinal damage and vision loss can be substantially decreased through early-stage diagnosis with computer-aided ocular disease diagnosis. With the use of color fundus photography for obtaining digital retinal fundus images, there is a growth in the online accessibility of digital fundus images. For diagnosing ocular diseases, an attempt was made to model graphs from images for feature learning. Three feature detection algorithms, namely scale-invariant feature transform, binary robust invariant scalable keypoints and oriented fast and rotated BRIEF (ORB) techniques are computed individually. As graphs are represented in the non-Euclidean domain, the graph neural network is used to learn the node embedding to model the network for ocular disease diagnosis. Three distance measures: the Euclidean, Manhattan and Chebyshev distances, are computed for analyzing the discriminative power of the model. The proposed RDD-Net model is trained and evaluated on the ODIR-2019 dataset with eleven different performance indicators. The results show that mapping images to non-Euclidean geometric space have obtained a successful diagnosis of ocular diseases from digital fundus images. The ORB descriptor outperforms the other two feature descriptors as well as the existing algorithms for ocular disease diagnosis. Results of the Chebyshev distance measure show superior performance when compared to the other two distance measures based on computation time and performance evaluation metrics. The proposed RDD-Net achieves an F1-score of 0.9970 and a sensitivity of 0.9969 with the ORB descriptor and shows state-of-the-art performance.

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

Similar content being viewed by others

Availability of data and materials

The dataset used for the work is available in the ODIR-2019 https://odir2019.grand-challenge.org/introduction/.

References

  1. Bresnick, G.H., Mukamel, D.B., Dickinson, J.C., Cole, D.R.: A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy. Ophthalmology 107(1), 19–24 (2000)

    Article  Google Scholar 

  2. Wong, W.L., Su, X., Li, X., Cheung, C.M.G., Klein, R., Cheng, C.-Y., Wong, T.Y.: Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob. Health 2(2), e106–e116 (2014)

    Article  Google Scholar 

  3. Hashemi, H., Pakzad, R., Yekta, A., Aghamirsalim, M., Pakbin, M., Ramin, S., Khabazkhoob, M.: Global and regional prevalence of age-related cataract: a comprehensive systematic review and meta-analysis. Eye 34(8), 1357–1370 (2020)

    Article  Google Scholar 

  4. Allison, K., Patel, D., Alabi, O.: Epidemiology of glaucoma: the past, present, and predictions for the future. Cureus 12(11), e11686 (2020)

    Google Scholar 

  5. Chandrasekaran, R., Loganathan, B.: Retinopathy grading with deep learning and wavelet hyper-analytic activations. Vis. Comput. (2022). https://doi.org/10.1007/s00371-022-02489-z

    Article  Google Scholar 

  6. Zheng, Y., He, M., Congdon, N.: The worldwide epidemic of diabetic retinopathy. Indian J. Ophthalmol. 60(5), 428 (2012)

    Article  Google Scholar 

  7. Wen, G., Tarczy-Hornoch, K., McKean-Cowdin, R., Cotter, S.A., Borchert, M., Lin, J., Kim, J., Varma, R., M-EPEDS Group, et al.: Prevalence of myopia, hyperopia, and astigmatism in non-hispanic white and asian children: multi-ethnic pediatric eye disease study. Ophthalmology 120(10), 2109–2116 (2013)

    Article  Google Scholar 

  8. Mills, K.T., Stefanescu, A., He, J.: The global epidemiology of hypertension. Nat. Rev. Nephrol. 16(4), 223–237 (2020)

    Article  Google Scholar 

  9. Abràmoff, M.D., Reinhardt, J.M., Russell, S.R., Folk, J.C., Mahajan, V.B., Niemeijer, M., Quellec, G.: Automated early detection of diabetic retinopathy. Ophthalmology 117(6), 1147–1154 (2010)

    Article  Google Scholar 

  10. Priya, R., Aruna, P.: Svm and neural network based diagnosis of diabetic retinopathy. Int. J. Comput. Appl. 41(1), 6–12 (2012)

    Google Scholar 

  11. Acharya, R., Chua, C.K., Ng, E., Yu, W., Chee, C.: Application of higher order spectra for the identification of diabetes retinopathy stages. J. Med. Syst. 32(6), 481–488 (2008)

    Article  Google Scholar 

  12. Karthikeyan, R., Alli, P.: Feature selection and parameters optimization of support vector machines based on hybrid glowworm swarm optimization for classification of diabetic retinopathy. J. Med. Syst. 42(10), 1–11 (2018)

    Article  Google Scholar 

  13. Sheet, S.S.M., Tan, T.-S., Asâari, M., Hitam, W.H.W., Sia, J.S.: Retinal disease identification using upgraded clahe filter and transfer convolution neural network. ICT Express 8(1), 142–150 (2022)

    Article  Google Scholar 

  14. Sharma, S., Mehra, R.: Effect of layer-wise fine-tuning in magnification-dependent classification of breast cancer histopathological image. Vis. Comput. 36(9), 1755–1769 (2020)

    Article  Google Scholar 

  15. Ahmad, N., Asghar, S., Gillani, S.A.: Transfer learning-assisted multi-resolution breast cancer histopathological images classification. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02153-y

    Article  Google Scholar 

  16. Islam, M.T., Imran, S.A., Arefeen, A., Hasan, M., Shahnaz, C. (2019). Source and camera independent ophthalmic disease recognition from fundus image using neural network. In: 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON), IEEE, pp. 59–63

  17. Jordi, C., Joan Manuel, N., Carles, V.: Ocular disease intelligent recognition through deep learning architectures. Universitat Oberta de Catalunya: Barcelona, Spain.

  18. Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)

    Article  Google Scholar 

  19. Grassmann, F., Mengelkamp, J., Brandl, C., Harsch, S., Zimmermann, M.E., Linkohr, B., Peters, A., Heid, I.M., Palm, C., Weber, B.H.: A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 125(9), 1410–1420 (2018)

    Article  Google Scholar 

  20. Gómez-Valverde, J.J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., Sánchez, C.I., Ledesma-Carbayo, M.J.: Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomed. Opt. Express 10(2), 892–913 (2019)

    Article  Google Scholar 

  21. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  22. Sharma, S., Mehra, R.: Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images—a comparative insight. J. Digit. Imag. 33(3), 632–654 (2020)

    Article  Google Scholar 

  23. Kensert, A., Harrison, P.J., Spjuth, O.: Transfer learning with deep convolutional neural networks for classifying cellular morphological changes. SLAS Discov. Adv. Life Sci. R&D 24(4), 466–475 (2019)

    Article  Google Scholar 

  24. Jafri, R., Ali, S.A., Arabnia, H.R., Fatima, S.: Computer vision-based object recognition for the visually impaired in an indoors environment: a survey. Vis. Comput. 30(11), 1197–1222 (2014)

    Article  Google Scholar 

  25. Zhang, W., Lin, Z., Cheng, J., Ma, C., Deng, X., Wang, H.: Sta-gcn: two-stream graph convolutional network with spatial–temporal attention for hand gesture recognition. Vis. Comput. 36(10), 2433–2444 (2020)

    Article  Google Scholar 

  26. Shibuya, E., Hotta, K.: Cell image segmentation by using feedback and convolutional lstm. Vis. Comput. 1–11 (2021)

  27. Gupta, S., Thakur, K., Kumar, M.: 2d-human face recognition using sift and surf descriptors of faces feature regions. Vis. Comput. 37(3), 447–456 (2021)

    Article  Google Scholar 

  28. Chhabra, P., Garg, N.K., Kumar, M.: Content-based image retrieval system using orb and sift features. Neural Comput. Appl. 32(7), 2725–2733 (2020)

    Article  Google Scholar 

  29. Luo, D., Kamata, S.-I.: Diabetic retinopathy grading based on lesion correlation graph. In: 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), IEEE, pp. 1–7 (2020)

  30. Urban, S., Weinmann, M.: Finding a good feature detector-descriptor combination for the 2d keypoint-based registration of tls point clouds. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences 2.

  31. Gauglitz, S., Höllerer, T., Turk, M.: Evaluation of interest point detectors and feature descriptors for visual tracking. Int. J. Comput. Vis. 94(3), 335 (2011)

    Article  MATH  Google Scholar 

  32. Pusztai, Z., Hajder, L.: Quantitative comparison of feature matchers implemented in opencv3. In: Proceedings of the 21st Computer Vision Winter Workshop, Rimske Toplice, Szlovénia, pp. 1–9 (2016)

  33. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)

    Article  Google Scholar 

  34. Morris, C., Ritzert, M., Fey, M., Hamilton, W.L., Lenssen, J.E., Rattan, G., Grohe, M.: Weisfeiler and leman go neural: higher-order graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4602–4609 (2019).

  35. Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: Thirty-second AAAI conference on artificial intelligence (2018)

  36. Leman, A., Weisfeiler, B.: A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsiya 2(9), 12–16 (1968)

    Google Scholar 

  37. Lin, J., Cai, Q., Lin, M.: Multi-label classification of fundus images with graph convolutional network and self-supervised learning. IEEE Signal Process. Lett. 28, 454–458 (2021)

    Article  Google Scholar 

  38. Li, Q., Peng, X., Qiao, Y., Peng, Q.: Learning label correlations for multi-label image recognition with graph networks. Pattern Recogn. Lett. 138, 378–384 (2020)

    Article  Google Scholar 

  39. Lin, Y., Ye, Q.: Support vector machine classifiers by non-euclidean margins. Math. Found. Comput. 3(4), 279 (2020)

    Article  MATH  Google Scholar 

  40. Cheng, Y., Ma, M., Li, X., Zhou, Y.: Multi-label classification of fundus images based on graph convolutional network. BMC Med. Inform. Decis. Mak. 21(2), 1–9 (2021)

    Google Scholar 

  41. Lowe, G.: Sift-the scale invariant feature transform. Int. J 2(91–110), 2 (2004)

    Google Scholar 

  42. Leutenegger, S., Chli, M., Siegwart, R.Y.: Brisk: binary robust invariant scalable keypoints. In: 2011 International conference on computer vision, Ieee, pp. 2548–2555 (2011)

  43. Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: European conference on Computer vision, Springer, pp. 183–196 (2010)

  44. Rublee, E, Rabaud, V, Konolige, K, Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 International conference on computer vision, Ieee, pp. 2564–2571 (2011)

  45. Viswanathan, D.G.: Features from accelerated segment test (fast). In: Proceedings of the 10th workshop on image analysis for multimedia interactive services, London, UK, pp. 6–8 (2009)

  46. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: binary robust independent elementary features. In: European conference on computer vision, Springer, pp. 778–792 (2010)

  47. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization arXiv:1412.6980

  48. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural. Inf. Process. Syst. 32, 8026–8037 (2019)

    Google Scholar 

  49. Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P., Zheng, Y.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016)

    Article  Google Scholar 

  50. Dominic, N., Cenggoro, T.W., Budiarto, A., Pardamean, B., et al.: Transfer learning using inception-resnet-v2 model to the augmented neuroimages data for autism spectrum disorder classification. Commun. Math. Biol. Neurosci. 2021 (2021) Article–ID.

  51. Sarki, R., Michalska, S., Ahmed, K., Wang, H., Zhang, Y.: Convolutional neural networks for mild diabetic retinopathy detection: an experimental study, bioRxiv (2019) 763136.

  52. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1251–1258 (2017)

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

  54. Luo, X., Li, J., Chen, M., Yang, X., Li, X.: Ophthalmic disease detection via deep learning with a novel mixture loss function. IEEE J. Biomed. Health Informatics 25, 3332–3339 (2021)

    Article  Google Scholar 

Download references

Acknowledgements

National Supercomputing Mission (NSM) is acknowledged for providing computing resources for ’PARAM Shakti’ at IIT Kharagpur, which is administered by C-DAC and supported by the Ministry of Electronics and Information Technology (MeitY) and Department of Science and Technology (DST), Government of India.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India

    Amritha Abdul Salam & Manjunatha Mahadevappa

  2. Department of Computer Science, Union Christian College, Aluva, Kerala, 683102, India

    Asha Das

  3. Artificial Intelligence and Computer Vision Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi, Kerala, 682022, India

    Madhu S. Nair

Authors
  1. Amritha Abdul Salam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manjunatha Mahadevappa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asha Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Madhu S. Nair
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manjunatha Mahadevappa.

Ethics declarations

Conflict of interest

All authors declare that they do not have conflict of 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

Salam, A.A., Mahadevappa, M., Das, A. et al. RDD-Net: retinal disease diagnosis network: a computer-aided diagnosis technique using graph learning and feature descriptors. Vis Comput 39, 4657–4670 (2023). https://doi.org/10.1007/s00371-022-02615-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-022-02615-x

Keywords

Search

Navigation