Developments for Diabetic Retinopathy Detection and Classification: A Review

  • Authors

    • Pooja M. Pawar
    • Avinash J. Agrawal
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.20029
  • CNN, deep learning, diabetic retinopathy, fundus images, retina.

  • Abstract

    Diabetes is characterized by impaired metabolism of glucose caused by insulin deficiency. Diabetic retinopathy is the eye disease, is caused by retinal damage which is generally formed as a result of diabetes mellitus. It is a serious vascular disorder for which early detection and the treatment are required to inhibit the intense vision loss. Also, the diagnosis entails skilled professionals for detection because non-automatic screening methods are very time consuming and are not that efficient for a large number of retinal images. This paper provides a broad review of various techniques and methodologies used by the authors for diabetic retinopathy detection and classification. Furthermore, most recent work and developments are studied in this paper. We are proposing an advanced deep learning CNN approach for automatic diagnosis of DR from color fundus images.

     

     

  • References

    1. [1] G.Gardner, D.Keating, T.H.Willamson, A.T.Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening Toolâ€, Brithish Journal of Opthalmology,(1996);80:940-944

      [2] R.Priya, P.Aruna, “Diagnosis of diabetic retinopathy using machine learning techniquesâ€, ICTACT Journal On Soft Computing, July 2013, Volume: 03, Issue: 04.

      [3] S.Roychowdhury, D.D.Koozekanani, Keshab K.Parhi, “DREAM: Diabetic Retinopathy Analysis Using Machine Learningâ€, IEEE Journal of BioMedical and Health Informatics, Vol.18, No 5, September (2014).

      [4] M.Rajesh Babu, BVNR Siva Kumar, P.Rakesh Kumar, “Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM Classifierâ€, September 2016 | IJIRT | Vol. 3, Issue 4 | ISSN: 2349-6002.

      [5] Mahendran Gandhi, Dr. R. Dhanasekaran, “Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM Classifier†IEEE International conference on Communication and Signal Processing, April 3-5, 2013, India.

      [6] Jaydeep De, Li Cheng, Xiaowei Zhang, Feng Lin, Huiqi Li, Kok Haur Ong, Weimiao Yu, Yuanhong Yu, and Sohail Ahmed, “A Graph-Theoretical Approach for Tracing Filamentary Structures in Neuronal and Retinal Imagesâ€, IEEE Transactions On Medical Imaging, Vol. 35, No. 1, January 2016.

      [7] Michael D. Abrà moff, Mona K. Garvin, Milan Sonka, “Retinal Imaging and Image Analysisâ€, IEEE Reviews in biomedical engineering, vol. 3, 2010.

      [8] Nair, V., Hinton, G.E..“Rectified linear units improve restricted boltzmann machinesâ€. In: Proceedings of the 27th International Conferenceon Machine Learning (ICML-10). 2010, p. 807–814.

      [9] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever,Ruslan Salakhutdinov. “Dropout: A Simple Way to Prevent NeuralNetworks from Overfittingâ€. Journal of Machine Learning Research15 (2014).

      [10] J. Nayak, P. Bhat, U. R. Acharya, C. M. Lim, M. Kagathi, “Automated identification of diabetic retinopathy stages using digital fundus images,†J. Med. Syst., vol. 32, 2008, pp. 107–115.

      [11] M.R.K. Mookiah, U. R. Acharya, R. J. Martis, C. K. Chua, L. C. Min, E. Y. K. Ng, A.Laude, “Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach,†Knowl. Based Syst., vol. 39(0), 2013, pp. 9–22.

      [12] J. J. Staal, M.D. Abranoff, M. Niemeijer, M.A. Viergener, B. van Ginneken, “Ridge based vessel segmentation in color images of the retina,†IEEE Trans. Medical Imaging, Vol. 23, pp. 501-509, 2004.

      [13] Mookiah, M.R.K., Acharya, U.R., Chua, C.K., Lim, C.M., Ng, E., Laude, “A..Computer-aided diagnosis of diabetic retinopathy: A reviewâ€. Comput Biol Med 2013;43(12):2136–2155.

      [14] A Mendulkar, R Kale, A Agrawal, “A survey on efficient human fall detection systemâ€, International journal of scientific & technology research, 2014.

      [15] V Mal, AJ Agrawal, “Removing Flaming Problems from Social Networking Sites using Semi-Supervised Learning Approachâ€, Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies,2016.

  • Downloads

  • How to Cite

    M. Pawar, P., & J. Agrawal, A. (2018). Developments for Diabetic Retinopathy Detection and Classification: A Review. International Journal of Engineering & Technology, 7(4.5), 134-137. https://doi.org/10.14419/ijet.v7i4.5.20029

    Received date: 2018-09-22

    Accepted date: 2018-09-22

    Published date: 2018-09-22