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Hybrid Deep Learning Models for Diabetic Retinopathy Classification

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Proceedings of the 5th International Conference on Big Data and Internet of Things (BDIoT 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 489))

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Abstract

Diabetic retinopathy is a complication of diabetes in the eye. This disease is caused by the damage of the blood vessels of the back of eye (i.e., retina). Unfortunately, diabetic retinopathy can cause several symptoms, the most serious of which is complete vision loss. Indeed, the detection of diabetic retinopathy is a time-consuming manual process that requires a qualified clinician to examine and evaluate digital color photographs of the retina’s fundus.

Currently, several researches are looking to employ artificial intelligence techniques, especially the Deep Learning, to deal with this issue. In this paper, we study some hybrid models for diabetic retinopathy severity classification in distributed and non-distributed environments. The studied models perform two main tasks: deep feature extraction and then classification of diabetic retinopathy according to its severity. The models were trained and validated on a publicly available dataset of 80,000 images and they achieved an accuracy of 80.7%.

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References

  1. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)

  2. Acharya, U.R., Lim, C.M., Ng, E.Y.K., Chee, C., Tamura, T.: Computer-based detection of diabetes retinopathy stages using digital fundus images. Proc. Inst. Mech. Eng. Part H: J. Eng. Med. 223(5), 545–553 (2009)

    Article  Google Scholar 

  3. Al-Rawi, M., Karajeh, H.: Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images. Comput. Methods Program. Biomed. 87(3), 248–253 (2007)

    Article  Google Scholar 

  4. Alban, M., Gilligan, T.: Automated detection of diabetic retinopathy using fluorescein angiography photographs. Report of Standford Education (2016)

    Google Scholar 

  5. Asiri, N., Hussain, M., Al Adel, F., Alzaidi, N.: Deep learning based computer-aided diagnosis systems for diabetic retinopathy: a survey. Artif. Intell. Med. 99, 101701 (2019)

    Article  Google Scholar 

  6. Bravo, M.A., Arbeláez, P.A.: Automatic diabetic retinopathy classification. In: 13th International Conference on Medical Information Processing and Analysis, vol. 10572, p. 105721E. International Society for Optics and Photonics (2017)

    Google Scholar 

  7. Carrera, E.V., González, A., Carrera, R.: Automated detection of diabetic retinopathy using SVM. In: 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pp. 1–4. IEEE (2017)

    Google Scholar 

  8. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989)

    Article  Google Scholar 

  9. Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016)

    Article  Google Scholar 

  10. Cho, N., et al.: IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res. Clin. Pract. 138, 271–281 (2018)

    Article  Google Scholar 

  11. Cuadros, J., Bresnick, G.: EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. J. Diab. Sci. Technol. 3(3), 509–516 (2009)

    Article  Google Scholar 

  12. Fleming, A.D., Philip, S., Goatman, K.A., Williams, G.J., Olson, J.A., Sharp, P.F.: Automated detection of exudates for diabetic retinopathy screening. Phys. Med. Biol. 52(24), 7385 (2007)

    Article  Google Scholar 

  13. Fong, D.S., et al.: Retinopathy in diabetes. Diab. Care 27(suppl 1), s84–s87 (2004)

    Article  Google Scholar 

  14. Goldberg, Y.: Neural network methods for natural language processing. Synthesis Lect. Hum. Lang. Technol. 10(1), 1–309 (2017)

    Article  Google Scholar 

  15. Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  16. Gulshan, V., 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 

  17. Guo, Y., Zou, B., Chen, Z., He, Q., Liu, Q., Zhao, R.: Optic cup segmentation using large pixel patch based CNNs (2016)

    Google Scholar 

  18. Jayashree, J., Sruthi, R., Ponnamanda Venkata, S., Vijayashree, J.: Knowledge based expert system for predictingdiabetic retinopathy using machine learningalgorithms. Int. J. Eng. Adv. Technol. (IJEAT) 9(4) (2020)

    Google Scholar 

  19. Lim, G., Cheng, Y., Hsu, W., Lee, M.L.: Integrated optic disc and cup segmentation with deep learning. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 162–169. IEEE (2015)

    Google Scholar 

  20. Mansour, R.F.: Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed. Eng. Lett. 8(1), 41–57 (2018)

    Article  Google Scholar 

  21. Meddad, M., Moujahdi, C., Mikram, M., Rziza, M.: A hybrid face identification system using a compressed CNN in a big data environment for embedded devices. Int. J. Comput. Digit. Syst. 9(4), 689–701 (2020)

    Article  Google Scholar 

  22. Nayak, J., Bhat, P.S., Acharya, R., Lim, C.M., Kagathi, M.: Automated identification of diabetic retinopathy stages using digital fundus images. J. Med. Syst. 32(2), 107–115 (2008)

    Article  Google Scholar 

  23. Paing, M.P., Choomchuay, S., Yodprom, M.R.: Detection of lesions and classification of diabetic retinopathy using fundus images. In: 2016 9th Biomedical Engineering International Conference (BMEiCON), pp. 1–5. IEEE (2016)

    Google Scholar 

  24. Qomariah, D.U.N., Tjandrasa, H., Fatichah, C.: Classification of diabetic retinopathy and normal retinal images using CNN and SVM. In: 2019 12th International Conference on Information & Communication Technology and System (ICTS), pp. 152–157. IEEE (2019)

    Google Scholar 

  25. Rahim, S.S., Palade, V., Jayne, C., Holzinger, A., Shuttleworth, J.: Detection of diabetic retinopathy and maculopathy in eye fundus images using fuzzy image processing. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds.) BIH 2015. LNCS (LNAI), vol. 9250, pp. 379–388. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23344-4_37

    Chapter  Google Scholar 

  26. Rhanoui, M., Mikram, M., Yousfi, S., Barzali, S.: A CNN-BiLSTM model for document-level sentiment analysis. Mach. Learn. Knowl. Extract. 1(3), 832–847 (2019)

    Article  Google Scholar 

  27. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  28. Scarpa, G., Gargiulo, M., Mazza, A., Gaetano, R.: A CNN-based fusion method for feature extraction from sentinel data. Remote Sen. 10(2), 236 (2018)

    Article  Google Scholar 

  29. Sevastopolsky, A.: Optic disc and cup segmentation methods for glaucoma detection with modification of U-net convolutional neural network. Pattern Recognit. Image Anal. 27(3), 618–624 (2017)

    Article  Google Scholar 

  30. Sinthanayothin, C., et al.: Automated detection of diabetic retinopathy on digital fundus images. Diabet. Med. 19(2), 105–112 (2002)

    Article  Google Scholar 

  31. Soares, J.V., Leandro, J.J., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)

    Article  Google Scholar 

  32. Sopharak, A., Uyyanonvara, B., Barman, S., Williamson, T.H.: Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput. Med. Imaging Graph. 32(8), 720–727 (2008)

    Article  Google Scholar 

  33. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)

    Google Scholar 

  34. Tan, J.H., Acharya, U.R., Bhandary, S.V., Chua, K.C., Sivaprasad, S.: Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J. Comput. Sci. 20, 70–79 (2017)

    Article  Google Scholar 

  35. Wang, Z., Yin, Y., Shi, J., Fang, W., Li, H., Wang, X.: Zoom-in-net: deep mining lesions for diabetic retinopathy detection. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 267–275. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_31

    Chapter  Google Scholar 

  36. Wu, A., Xu, Z., Gao, M., Buty, M., Mollura, D.J.: Deep vessel tracking: a generalized probabilistic approach via deep learning. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1363–1367. IEEE (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LRIT Laboratory, Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco

    Mounia Mikram, Majdouline Meddad & Asmaa Khallout

  2. Scientific Institute of Rabat, Mohammed V University in Rabat, Rabat, Morocco

    Chouaib Moujahdi & Asmaa Khallout

  3. IMS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University in Rabat, Rabat, Morocco

    Maryem Rhanoui & Asmaa Khallout

  4. Meridian Team, LYRICA Laboratory, School of Information Sciences, Rabat, Morocco

    Mounia Mikram, Maryem Rhanoui & Asmaa Khallout

Authors
  1. Mounia Mikram
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  2. Chouaib Moujahdi
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  3. Maryem Rhanoui
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  4. Majdouline Meddad
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  5. Asmaa Khallout
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Corresponding author

Correspondence to Mounia Mikram .

Editor information

Editors and Affiliations

  1. ENSIAS, Mohammed V University, Rabat, Morocco

    Mohamed Lazaar

  2. UNILEHAVRE, UNIROUEN, Normandie Université, Le Havre, France

    Claude Duvallet

  3. Vrije Universiteit Brussel, Brussels, Belgium

    Abdellah Touhafi

  4. ENSA, Abdelmalek Essaâdi University, Tetuan, Morocco

    Mohammed Al Achhab

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Mikram, M., Moujahdi, C., Rhanoui, M., Meddad, M., Khallout, A. (2022). Hybrid Deep Learning Models for Diabetic Retinopathy Classification. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_13

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