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Exploratory Study on Direct Prediction of Diabetes Using Deep Residual Networks

  • Samaneh Abbasi-SureshjaniEmail author
  • Behdad Dashtbozorg
  • Bart M. ter Haar Romeny
  • François Fleuret
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 27)

Abstract

Diabetes is threatening the health of many people in the world. People may be diagnosed with diabetes only when symptoms or complications such as diabetic retinopathy start to appear. Retinal images reflect the health of the circulatory system and they are considered as a cheap and patient-friendly source of information for diagnosis purposes. Convolutional neural networks have enhanced the performance of conventional image processing techniques significantly by neglecting inconsistent feature extraction pipelines and learning informative features automatically from data. In this work we explore the possibility of using the deep residual networks as one of the state-of-the-art convolutional networks to diagnose diabetes directly from retinal images, without using any blood glucose information. The results indicate that convolutional networks are able to capture informative differences between healthy and diabetic patients and it is possible to differentiate between these two groups using only the retinal images. The performance of the proposed method is significantly higher than human experts.

Keywords

Retinal images Diabetes Diabetic retinopathy Deep learning ResNet 

Notes

Acknowledgements

This project has received funding from the European Union’s Seventh Framework Programme, Marie Curie Actions-Initial Training Network, under grant agreement No. 607643, “Metric Analysis For Emergent Technologies (MAnET)". The authors would like to thank Dr. Tos Berendschot and Dr. Jan Schouten from University Eye Clinic Maastricht for providing the fundus images and clinical data used during this research.

References

  1. 1.
    Abràmoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)CrossRefGoogle Scholar
  2. 2.
    American Diabetes Association: Diagnosis and classification of diabetes mellitus. Diabetes Care 33(Supplement 1), S62–S69 (2010)CrossRefGoogle Scholar
  3. 3.
    Cohen, J.: Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychol. Bull. 70(4), 213 (1968)CrossRefGoogle Scholar
  4. 4.
    Dashtbozorg, B., Abbasi-Sureshjani, S., Zhang, J., Huang, F., Bekkers, E., ter Haar Romeny, B.M.: Infrastructure for retinal image analysis. In: Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop (OMIA 2016) Held in Conjunction with MICCAI 2016 (2016)Google Scholar
  5. 5.
    Dashtbozorg, B., Zhang, J., Abbasi-Sureshjani, S., Huang, F., ter Haar Romeny, B.M.: Retinal health information and notification system (RHINO). In: SPIE Medical Imaging, pp. 1013437–1013437-6. International Society for Optics and Photonics (2017)Google Scholar
  6. 6.
    Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Aistats, vol. 15, p. 275 (2011)Google Scholar
  7. 7.
    Greenspan, H., van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)CrossRefGoogle Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)Google Scholar
  9. 9.
    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, pp. 770–778 (2016)Google Scholar
  10. 10.
    Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pp. 448–456 (2015)Google Scholar
  11. 11.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  12. 12.
    Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesan, K., Constable, I.J.: Retinal image analysis: concepts, applications and potential. Prog. Retinal Eye Res. 25(1), 99–127 (2006)CrossRefGoogle Scholar
  13. 13.
    Schram, M.T., Sep, S.J., van der Kallen, C.J., Dagnelie, P.C., Koster, A., Schaper, N., Henry, R.M., Stehouwer, C.D.: The maastricht study: an extensive phenotyping study on determinants of type 2 diabetes, its complications and its comorbidities. Eur. J. Epidemiol. 29(6), 439–451 (2014)CrossRefGoogle Scholar
  14. 14.
    Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19(1) (2017)Google Scholar
  15. 15.
    Wilkinson, C., Ferris, F.L., Klein, R.E., Lee, P.P., Agardh, C.D., Davis, M., Dills, D., Kampik, A., Pararajasegaram, R., Verdaguer, J.T., et al.: Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110(9), 1677–1682 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Samaneh Abbasi-Sureshjani
    • 1
    Email author
  • Behdad Dashtbozorg
    • 1
  • Bart M. ter Haar Romeny
    • 1
    • 2
  • François Fleuret
    • 3
  1. 1.Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Department of Biomedical and Information EngineeringNortheastern UniversityShenyangChina
  3. 3.Machine Learning GroupIdiap Research InstituteMartignySwitzerland

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