Call for Paper - November 2021 Edition
IJCA solicits original research papers for the November 2021 Edition. Last date of manuscript submission is October 20, 2021. Read More

Automated Detection of Diabetic Retinopathy using Deep Residual Learning

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Authors:
Md Ashikur Rahman, Md Arifur Rahman, Juena Ahmed Noshin
10.5120/ijca2020919927

Md Ashikur Rahman, Md Arifur Rahman and Juena Ahmed Noshin. Automated Detection of Diabetic Retinopathy using Deep Residual Learning. International Journal of Computer Applications 177(42):25-32, March 2020. BibTeX

@article{10.5120/ijca2020919927,
	author = {Md Ashikur Rahman and Md Arifur Rahman and Juena Ahmed Noshin},
	title = {Automated Detection of Diabetic Retinopathy using Deep Residual Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2020},
	volume = {177},
	number = {42},
	month = {Mar},
	year = {2020},
	issn = {0975-8887},
	pages = {25-32},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume177/number42/31185-2020919927},
	doi = {10.5120/ijca2020919927},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Significant amount of people suffer from Diabetic Retinopathy (DR), which is one of the major causes of vision loss. The incidence of this disease is even higher due to not being diagnosed at the right time. On numerous occasions, due to neglect and poor care, diabetic retinopathy can lead to significant damage to the eyes. That is why, early diagnosis of eye diseases, proper treatment and care for the disease can prevent vision loss. Referral of eyes with diabetic retinopathy for advanced assessment and treatment would aid in reducing the chances of vision loss, allowing proper diagnoses. The purpose of this study is to develop resilient and flexible diagnostic techniques for the detection of DR and to identify dynamic DR grading using residual networks to facilitate the network training that are significantly intense than previously used networks. Even though lots of research has been done on DR, its identifications remains challenging due to time and space complexity along with higher accuracy specificity. Here, a residual learning framework has been proposed that overcomes the challenges while efficiently detecting DR. Hence, using a high-end Graphics Processor Unit (GPU) the model has been trained on the publicly available Kaggle dataset and empirical evidence has been provided in order to support the results with a sensitivity of 95.6% and an accuracy of 93.20%.

References

  1. Gurudath, Nikita, Celenk, Mehmet and Riley, H. 2015. Machine learning identification of diabetic retinopathy from fundus images. 2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 - Proceedings. 10.1109/SPMB.2014.7002949.
  2. Joshi, S. and Karule, P.T., 2012. Retinal blood vessel segmentation. International Journal of Engineering and Innovative Technology (IJEIT), 1(3), pp.175-178.
  3. Dhanushkodi, S.S.R. and Vasuki, M., 2013. Diagnosis system for diabetic retinopathy to prevent vision loss. Applied Medical Informatics, 33(3), pp.1-11.
  4. Sevik, U., Kose, C., Berber, T. and Erdol, H., 2014. Identification of suitable fundus images using automated quality assessment methods. Journal of biomedical optics, 19(4), p.046006.
  5. Sujith Kumar, S.B. and Singh, V., 2012. Automatic detection of diabetic retinopathy in non-dilated RGB retinal fundus images. International Journal of Computer Applications, 47(19).
  6. Abràmoff, M.D., Folk, J.C., Han, D.P., Walker, J.D., Williams, D.F., Russell, S.R., Massin, P., Cochener, B., Gain, P., Tang, L. and Lamard, M., 2013. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA ophthalmology, 131(3), pp.351-357.
  7. Priya, R. and Aruna, P., 2012. SVM and neural network based diagnosis of diabetic retinopathy. International Journal of Computer Applications, 41(1).
  8. Bragge, P., Gruen, R.L., Chau, M., Forbes, A. and Taylor, H.R., 2011. Screening for presence or absence of diabetic retinopathy: a meta-analysis. Archives of Ophthalmology, 129(4), pp.435-444.
  9. Casanova, R., Saldana, S., Chew, E.Y., Danis, R.P., Greven, C.M. and Ambrosius, W.T., 2014. Application of random forests methods to diabetic retinopathy classification analyses. PLoS One, 9(6), p.e98587.
  10. Garg, S., Jani, P.D., Kshirsagar, A.V., King, B. and Chaum, E., 2012. Telemedicine and retinal imaging for improving diabetic retinopathy evaluation. Archives of internal medicine, 172(21), pp.1677-1680.
  11. Lazar, I., Qureshi, R.J. and Hajdu, A., 2010, October. A novel approach for the automatic detection of micro aneurysms in retinal images. In 2010 6th International Conference on Emerging Technologies (ICET) (pp. 193-197).IEEE.
  12. Sánchez, C.I., Hornero, R., López, M.I., Aboy, M., Poza, J. and Abásolo, D., 2008. A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. Medical engineering & physics, 30(3), pp.350-357.
  13. Sinthanayothin, C., Boyce, J.F., Williamson, T.H., Cook, H.L., Mensah, E., Lal, S. and Usher, D., 2002. Automated detection of diabetic retinopathy on digital fundus images. Diabetic medicine, 19(2), pp.105-112.
  14. Torok, Z., Peto, T., Csosz, E., Tukacs, E., Molnar, A., Maros-Szabo, Z., Berta, A., Tozser, J., Hajdu, A., Nagy, V. and Domokos, B., 2013. Tear fluid proteomics multimarkers for diabetic retinopathy screening. BMC ophthalmology, 13(1), p.40.
  15. Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J. and Kim, R., 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), pp.2402-2410.
  16. Gargeya, R. and Leng, T., 2017. Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7), pp.962-969.
  17. Poplin, R., Varadarajan, A.V., Blumer, K., Liu, Y., McConnell, M.V., Corrado, G.S., Peng, L. and Webster, D.R., 2017. Predicting cardiovascular risk factors from retinal fundus photographs using deep learning. arXiv 2017. arXiv preprint arXiv:1708.09843.
  18. Abràmoff, M.D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J.C. and Niemeijer, M., 2016. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative ophthalmology & visual science, 57(13), pp.5200-5206.
  19. Ting, D.S.W., Cheung, C.Y.L., Lim, G., Tan, G.S.W., Quang, N.D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I.Y., Lee, S.Y. and Wong, E.Y.M., 2017. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama, 318(22), pp.2211-2223.
  20. Xu, K., Feng, D. and Mi, H., 2017. Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules, 22(12), p.2054.
  21. Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P. and Zheng, Y., 2016. Convolutional neural networks for diabetic retinopathy. Procedia Computer Science, 90, pp.200-205.
  22. Yang, Y., Li, T., Li, W., Wu, H., Fan, W. and Zhang, W., 2017, September. Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 533-540). Springer, Cham.
  23. Gondal, W.M., Köhler, J.M., Grzeszick, R., Fink, G.A. and Hirsch, M., 2017, September. Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. In 2017 IEEE International Conference on Image Processing (ICIP) (pp. 2069-2073).IEEE.
  24. Quellec, G., Charrière, K., Boudi, Y., Cochener, B. and Lamard, M., 2017. Deep image mining for diabetic retinopathy screening. Medical image analysis, 39, pp.178-193.
  25. Choi, J.Y., Yoo, T.K., Seo, J.G., Kwak, J., Um, T.T. and Rim, T.H., 2017. Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database. PloS one, 12(11), p.e0187336.
  26. Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C., Liang, H., Baxter, S.L., McKeown, A., Yang, G., Wu, X., Yan, F. and Dong, J., 2018. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), pp.1122-1131.
  27. Wang, Z., Yin, Y., Shi, J., Fang, W., Li, H. and Wang, X., 2017, September. Zoom-in-net: Deep mining lesions for diabetic retinopathy detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 267-275). Springer, Cham.
  28. García, G., Gallardo, J., Mauricio, A., López, J. and Del Carpio, C., 2017, September. Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images. In International Conference on Artificial Neural Networks (pp. 635-642).Springer, Cham.
  29. Costa, P. and Campilho, A., 2017. Convolutional bag of words for diabetic retinopathy detection from eye fundus images. IPSJ Transactions on Computer Vision and Applications, 9(1), p.10.
  30. Wang, J. and Olson, E., 2016, October. AprilTag 2: Efficient and robust fiducial detection. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4193-4198).IEEE.
  31. Feinman, R., Curtin, R.R., Shintre, S. and Gardner, A.B., 2017. Detecting adversarial samples from artifacts.arXiv preprint arXiv:1703.00410.
  32. Zhang, W., Zhao, Y., Breckon, T.P. and Chen, L., 2017. Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels. Pattern Recognition, 63, pp.193-205.
  33. Zhang, J., Zhang, T., Dai, Y., Harandi, M. and Hartley, R., 2018. Deep unsupervised saliency detection: A multiple noisy labeling perspective. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 9029-9038).
  34. Laddha, A., Kocamaz, M.K., Navarro-Serment, L.E. and Hebert, M., 2016, June. Map-supervised road detection. In 2016 IEEE Intelligent Vehicles Symposium (IV) (pp. 118-123).IEEE.
  35. Kaur, S. and Singh, I., 2016. Comparison between edge detection techniques. International Journal of Computer Applications, 145(15), pp.15-18.
  36. Routray, S., Ray, A.K. and Mishra, C., 2017, February. Analysis of various image feature extraction methods against noisy image: SIFT, SURF and HOG. In 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-5). IEEE.

Keywords

Diabetic Retinopathy, Deep Neural Network, Residual Learning, Image Recognition