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DRDr: Automatic Masking of Exudates and Microaneurysms Caused by Diabetic Retinopathy Using Mask R-CNN and Transfer Learning

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Advances in Computer Vision and Computational Biology

Abstract

This paper addresses the problem of identifying two main types of lesions—Exudates and Microaneurysms—caused by Diabetic Retinopathy (DR) in the eyes of diabetic patients. We make use of Convolutional Neural Networks (CNNs) and Transfer Learning to locate and generate high-quality segmentation mask for each instance of the lesion that can be found in the patients’ fundus images. We create our normalized database out of e-ophtha EX and e-ophtha MA and tweak Mask R-CNN to detect small lesions. Moreover, we employ data augmentation and the pre-trained weights of ResNet101 to compensate for our small dataset. Our model achieves promising test mAP of 0.45, altogether showing that it can aid clinicians and ophthalmologist in the process of detecting and treating the infamous DR.

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References

  1. P. Massin, A. Chabouis, A. Erginay, C. Viens-Bitker, A. Lecleire-Collet, T. Meas, P.-J. Guillausseau, G. Choupot, B. André, P. Denormandie, OphdiatⒸ: a telemedical network screening system for diabetic retinopathy in the île-de-france. Diabetes Metab. 34(3), 227–234 (2008)

    Article  Google Scholar 

  2. E. Decencière, G. Cazuguel, X. Zhang, G. Thibault, J.-C. Klein, F. Meyer, B. Marcotegui, G. Quellec, M. Lamard, R. Danno, et al., Teleophta: Machine learning and image processing methods for teleophthalmology. Irbm 34(2), 196–203 (2013)

    Article  Google Scholar 

  3. K. Bhatia, S. Arora, R. Tomar, Diagnosis of diabetic retinopathy using machine learning classification algorithm, in 2016 2nd International Conference on Next Generation Computing Technologies (NGCT) (IEEE, Piscataway, 2016), pp. 347–351

    Google Scholar 

  4. B. Antal, A. Hajdu, An ensemble-based system for automatic screening of diabetic retinopathy. Knowledge-Based Syst. 60, 20–27 (2014)

    Article  Google Scholar 

  5. D. Usher, M. Dumskyj, M. Himaga, T.H. Williamson, S. Nussey, J. Boyce, Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabetic Med. 21(1), 84–90 (2004)

    Article  Google Scholar 

  6. G. Gardner, D. Keating, T.H. Williamson, A.T. Elliott, Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. British J. Ophthalmol. 80(11), 940–944 (1996)

    Article  Google Scholar 

  7. R. Priya, P. Aruna, Diagnosis of diabetic retinopathy using machine learning techniques. ICTACT J. Soft Comput. 3(4), 563–575 (2013)

    Article  Google Scholar 

  8. A. Sopharak, M.N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K.T. Nwe, Y.A. Moe, Machine learning approach to automatic exudate detection in retinal images from diabetic patients. J. Modern Optics 57(2), 124–135 (2010)

    Article  Google Scholar 

  9. Z. Liu, C. Opas, S.M. Krishnan, Automatic image analysis of fundus photograph, in Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.’Magnificent Milestones and Emerging Opportunities in Medical Engineering’(Cat. No. 97CH36136), vol. 2 (IEEE, Piscataway, 1997), pp. 524–525

    Google Scholar 

  10. B.M. Ege, O.K. Hejlesen, O.V. Larsen, K. Møller, B. Jennings, D. Kerr, D.A. Cavan, Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput. Methods Program Biomed. 62(3), 165–175 (2000)

    Article  Google Scholar 

  11. C. Sinthanayothin, J.F. Boyce, H.L. Cook, T.H. Williamson, Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. British J. Ophthalmol. 83(8), 902–910 (1999)

    Article  Google Scholar 

  12. J. Lachure, A. Deorankar, S. Lachure, S. Gupta, R. Jadhav, Diabetic retinopathy using morphological operations and machine learning, in 2015 IEEE International Advance Computing Conference (IACC) (IEEE, Piscataway, 2015), pp. 617–622

    Book  Google Scholar 

  13. S. Roychowdhury, D.D. Koozekanani, K.K. Parhi, Dream: diabetic retinopathy analysis using machine learning. IEEE J. Biomed. Health Inf. 18(5), 1717–1728 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. P. Adarsh, D. Jeyakumari, Multiclass SVM-based automated diagnosis of diabetic retinopathy, in 2013 International Conference on Communication and Signal Processing (IEEE, Piscataway, 2013), pp. 206–210

    Book  Google Scholar 

  16. R. Gargeya, T. Leng, Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)

    Article  Google Scholar 

  17. V. Gulshan, L. Peng, M. Coram, M.C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, 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 

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

    Article  Google Scholar 

  19. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012), pp. 1097–1105

    Google Scholar 

  20. S. Amirian, K. Rasheed, T.R. Taha, H.R. Arabnia, Image captioning with generative adversarial network, in 2019 International Conference on Computational Science and Computational Intelligence (CSCI) (IEEE, Piscataway, 2019), pp. 272–275

    Google Scholar 

  21. N. Soans, E. Asali, Y. Hong, P. Doshi, SA-net: Robust state-action recognition for learning from observations, in IEEE International Conference on Robotics and Automation (ICRA) (2020), pp. 2153–2159

    Google Scholar 

  22. A. Das, S. Datta, G. Gkioxari, S. Lee, D. Parikh, D. Batra, Embodied question answering, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2018), pp. 2054–2063

    Google Scholar 

  23. F.G. Mohammadi, H.R. Arabnia, M.H. Amini, On parameter tuning in meta-learning for computer vision, in 2019 International Conference on Computational Science and Computational Intelligence (CSCI) (IEEE, Piscataway, 2019), pp. 300–305

    Google Scholar 

  24. S. Xie, A. Kirillov, R. Girshick, K. He, Exploring randomly wired neural networks for image recognition, in Proceedings of the IEEE International Conference on Computer Vision (2019), pp. 1284–1293

    Google Scholar 

  25. R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 580–587

    Google Scholar 

  26. R. Girshick, Fast R-CNN, in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 1440–1448

    Google Scholar 

  27. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems (2015), pp. 91–99

    Google Scholar 

  28. K. He, G. Gkioxari, P. Dollár, R. Girshick, Mask R-CNN, in Proceedings of the IEEE International Conference on Computer Vision (2017), pp. 2961–2969

    Google Scholar 

  29. F. Shenavarmasouleh, H. Arabnia, Causes of misleading statistics and research results irreproducibility: A concise review, in 2019 International Conference on Computational Science and Computational Intelligence (CSCI) (2019), pp. 465–470

    Google Scholar 

  30. G. Bradski, The OpenCV Library, Dr. Dobb’s Journal of Software Tools (2000)

    Google Scholar 

  31. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778

    Google Scholar 

  32. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, C.L. Zitnick, Microsoft coco: Common objects in context, in European Conference on Computer Vision (Springer, Berlin, 2014), pp. 740–755

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, USA

    Farzan Shenavarmasouleh & Hamid R. Arabnia

Authors
  1. Farzan Shenavarmasouleh
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  2. Hamid R. Arabnia
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Correspondence to Farzan Shenavarmasouleh .

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Editors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  2. School of Computing and Data Sciences, Wentworth Institute of Technology, Boston, MA, USA

    Leonidas Deligiannidis

  3. Graduate School of Information Science & Engineering, University of Electro-Communications, Chofu, Japan

    Hayaru Shouno

  4. Facultad de Informática - CIC PBA, Universidad Nacional de La Plata, La Plata, Argentina

    Fernando G. Tinetti

  5. Department of Computer Science, Southeastern Louisiana University, Hammond, LA, USA

    Quoc-Nam Tran

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Shenavarmasouleh, F., Arabnia, H.R. (2021). DRDr: Automatic Masking of Exudates and Microaneurysms Caused by Diabetic Retinopathy Using Mask R-CNN and Transfer Learning. In: Arabnia, H.R., Deligiannidis, L., Shouno, H., Tinetti, F.G., Tran, QN. (eds) Advances in Computer Vision and Computational Biology. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71051-4_24

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  • DOI: https://doi.org/10.1007/978-3-030-71051-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71050-7

  • Online ISBN: 978-3-030-71051-4

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