AI for Diabetic Retinopathy

M.M. Mastoli¹ , U.R. Pol² , Rahul D. Patil³

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.6 , pp.30-35, Dec-2019

Online published on Dec 31, 2019

Copyright © M.M. Mastoli, U.R. Pol, Rahul D. Patil . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Diabetes Mellitus is one of the widespread and increasing endocrine diseases. Many countries are working to prevent this disease at early stage by predicting the symptoms of diabetes using several methods. Many people with diabetes can have an eye complication called diabetic retinopathy. It is a diabetes disease that affects eyes. The main aim of this study is to model CNN to predict diabetic retinopathy using deep learning techniques. In this paper researchers compared and reviewed articles of deep learning for diagnosis diabetic retinopathy. These approaches have been tested with data samples. The performances of the model have been measured in terms of accuracy for the dataset with diabetic retinopathy and without diabetic retinopathy.

Key-Words / Index Term :
Artificial intelligence, Deep Learning, Diabetes Retinopathy, CNN, FastAI, Google Colab

References :
[1] Lee, R., Wong, T.Y., Sabanayagam, C., 2015. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis (Lond) 2, 17.
[2] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., Wang, Y., 2017. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2, 230–243.
[3] A.A.Alghadyan, Diabeticretinopathy—an update, Saudi J.Ophthalmol.25(1) (2011)99–111.
[4] U.R.Acharya, O.Faust, N.A.Kadri, J.S.Suri, W.Yu, Automated identification of normal and diabetes heart rate signals using online measures, Comput. Biol. Med.43(10)(2013)1523–1529.
[5] F. PĂ©rez and B. E. Granger, “Ipython: a system for interactive scientificcomputing,” Computing in Science & Engineering, vol. 9, no. 3, 2007.
[6] Colaboratory: Frequently Asked Questions, Jun. 2018, [online] Available: https://research.google.com/colaboratory/faq.html.
[7] T. Carneiro, R. V. Medeiros Da NĂłbrega, T. Nepomuceno, G. Bian, V. H. C. De Albuquerque and P. P. R. Filho, "Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications," in IEEE Access, vol. 6, pp. 61677-61685, 2018.
[8] Pessoa, Tiago & Medeiros, Raul & Nepomuceno, Thiago & Bian, Gui-Bin & Albuquerque, V.H.C. & Filho, Pedro Pedrosa. (2018). Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications. IEEE Access. PP. 1-1. 10.1109/ACCESS.2018.2874767.
[9] R. Casanova, S. Saldana, E. Y. Chew, R. P. Danis, C. M. Greven and W. T. Ambrosius, “Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses”, PLOS ONE, vol. 9, no. 6, (2014), pp. 1-7.
[10] C. Sinthanayothin, J.F. Boyce, T.H. Williamson, H.L. Cook, E. Mensah, S. Lal and D. Usher. “Automated Detection of Diabetic Retinopathy on Digital Fundus Images”, Diabetic Medicine, vol. 19, (2002), pp. 105-12.
[11] D. Usher, M. Dumskyjs, M. Himaga, T.H. Williamson, S. Nussey and J. Boyce, “Automated Detection of Diabetic Retinopathy in Digital Retinal Images: A Tool for Diabetic Retinopathy Screening”, Diabetic Medicine, vol. 21, (2003), pp. 84-90.
[12] https://docs.fast.ai/
[13] Muthu Rama Krishnan Mookiah, U.RajendraAcharya , ChuaKuangChua, Choo MinLim, E.Y.K.Ng, Augustinus Laude, “Computers in Biology and Medicine43(2013)2136–2155” Computers inBiologyandMedicine43(2013)2136–2155
[14] Ursula Schmidt-Erfurth, Amir Sadeghipour, Bianca S. Gerendas1, Sebastian M. Waldstein, Hrvoje Bogunovic, “Artificial intelligence in retina”Progress in Retinal and Eye Research 67 (2018) 1–29
[15] U Raghavendra , Hamido Fujita , Sulatha V Bhandary , Anjan Gudigar , Jen Hong Ta, U Rajendra Acharya “Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images”, Information Sciences 441 (2018) 41–49
[16] V. Raman, P. Then and P. Sumari. “Proposed retinal abnormality detection and classification approach: Computer-aided detection for diabetic retinopathy by machine learning approaches”, Communication Software and Networks (ICCSN), 2016 8th IEEE International Conference on. IEEE, (2016).
[17] Suvajit Dutta, Bonthala CS Manideep, Syed Muzamil Basha, Ronnie D. Caytiles and N. Ch. S. N. Iyengar “Classification of Diabetic Retinopathy Images by Using Deep Learning Models” International Journal of Grid and Distributed Computing Vol. 11, No. 1 (2018), pp.89-106
[18] N. Singh and R. Chandra Tripathi. “Automated early detection of diabetic retinopathy using image analysis techniques”, International Journal of Computer Applications, vol. 8, no. 2, (2010), pp. 18-23.
[19] Abr`amoff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57:5200– 5206. DOI:10.1167/iovs.16-19964
[20] T. A. Soomro, “Role of Image Contrast Enhancement Technique for Ophthalmologist as Diagnostic Tool for Diabetic Retinopathy”, Digital Image Computing: Techniques and Applications (DICTA), 2016 International Conference on. IEEE, (2016).
[21] G. G. Gardner, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool”, British journal of Ophthalmology, vol. 80, no. 11, (1996), pp. 940-944.
[22] M. U. Akram, “Detection and classification of retinal lesions for grading of diabetic retinopathy”, Computers in biology and medicine, vol. 45, (2014), pp. 161-171.
[23] R. J. Winder, “Algorithms for digital image processing in diabetic retinopathy”, Computerized Medical Imaging and Graphics, vol. 33, no.8, (2009), pp. 608-622.
[24] Y. Zhao, “Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy”, IEEE Transactions on Medical Imaging, vol. 36, no. 1, (2017), pp. 51-63.
[25] Harry Pratt,Frans Coenen, Deborah M Broadbent, Simon P Harding, Yalin Zhen ,“Convolutional Neural Networks for Diabetic Retinopathy”, Procedia Computer Science 90 ( 2016 ) 200 – 205