Open Access   Article Go Back

A Deep Learning Approach in Detecting Diabetic Retinopathy Using Convolutional Neural Network on Gaussian Filtered Retina Scanned images

P.S. Ezekiel1 , O.E. Taylor2 , F.B. Deedam-Okuchaba3

Section:Research Paper, Product Type: Journal Paper
Volume-8 , Issue-6 , Page no. 34-39, Jun-2020

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v8i6.3439

Online published on Jun 30, 2020

Copyright © P.S. Ezekiel, O.E. Taylor, F.B. Deedam-Okuchaba . 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.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

IEEE Style Citation: P.S. Ezekiel, O.E. Taylor, F.B. Deedam-Okuchaba, “A Deep Learning Approach in Detecting Diabetic Retinopathy Using Convolutional Neural Network on Gaussian Filtered Retina Scanned images,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.34-39, 2020.

MLA Style Citation: P.S. Ezekiel, O.E. Taylor, F.B. Deedam-Okuchaba "A Deep Learning Approach in Detecting Diabetic Retinopathy Using Convolutional Neural Network on Gaussian Filtered Retina Scanned images." International Journal of Computer Sciences and Engineering 8.6 (2020): 34-39.

APA Style Citation: P.S. Ezekiel, O.E. Taylor, F.B. Deedam-Okuchaba, (2020). A Deep Learning Approach in Detecting Diabetic Retinopathy Using Convolutional Neural Network on Gaussian Filtered Retina Scanned images. International Journal of Computer Sciences and Engineering, 8(6), 34-39.

BibTex Style Citation:
@article{Ezekiel_2020,
author = {P.S. Ezekiel, O.E. Taylor, F.B. Deedam-Okuchaba},
title = {A Deep Learning Approach in Detecting Diabetic Retinopathy Using Convolutional Neural Network on Gaussian Filtered Retina Scanned images},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2020},
volume = {8},
Issue = {6},
month = {6},
year = {2020},
issn = {2347-2693},
pages = {34-39},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5142},
doi = {https://doi.org/10.26438/ijcse/v8i6.3439}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i6.3439}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5142
TI - A Deep Learning Approach in Detecting Diabetic Retinopathy Using Convolutional Neural Network on Gaussian Filtered Retina Scanned images
T2 - International Journal of Computer Sciences and Engineering
AU - P.S. Ezekiel, O.E. Taylor, F.B. Deedam-Okuchaba
PY - 2020
DA - 2020/06/30
PB - IJCSE, Indore, INDIA
SP - 34-39
IS - 6
VL - 8
SN - 2347-2693
ER -

VIEWS PDF XML
475 539 downloads 251 downloads
  
  
           

Abstract

Diabetic retinopathy is a diabetes complication that affects eyes. It is caused by damage to the blood vessels of the light-sensitive tissue at the back of the eye (retina). At first, diabetic retinopathy may cause no symptoms or only mild vision problems however, it can cause blindness. The condition can develop in anyone who has type 1 or type 2 diabetes. It may lead to poor vision and subsequently to complete blindness. This paper presents a Deep Learning approach in detecting Diabetic Retinopathy on Gaussian Filtered Retina Scanned images. We used a Gaussian filtered scan retina image dataset which was downloaded from kaggle.com. This dataset contains five image folders which are Mild folder that contains 370 images of patients with lesser risk to Diabetic Retinopathy (early stage), Moderate Folder contains 999 images of patients having 12%-27% risk of Diabetic Retinopathy, the Severe Folder contains 193 images of patients whose blood vessels have become more blocked, the Proliferate Folder contains 295 images of patients which are on the verge of going on a permanent blindness, the last folder is the No Diabetic Retinopathy folder which contains 1805 images of patients who have no Diabetic Retinopathy. After building and training our convolutional neural network model, the results obtain by the model shows an accuracy of 81.35% at an epoch number of 8. The trained model was saved and tested using flask framework. This model can be deployed to web for detecting and classifying the various categories of diabetic retinopathy.

Key-Words / Index Term

Gaussian filtered images, Diabetic Retinopathy, Convolutional Neural Network

References

[1]. T.J. Wolfensberger and A.M. Hamilton ?Diabetic Retinopathy - An Historical Review? Seminar in Ophthalmology, Vol.16, issue1, pp. 2-7, 2001.
[2]. Y. JW, R. SL, R. Kawasaki, L. EL, K. JW, T. Bek, C. SJ, D. JM, A. Fletcher, J. Grauslund, S. Haffner, R.F. Hamman, M.k. Ikram , T. Kayama, B.E Klein, R. Klein, S. Krishnaiah, k. Mayurasakorn, J.R. O`Hare , T.J. Orchard, M. Porta, M. Rema, M.S. Roy, T. Sharma, J. Shaw, H. Taylor, J.M Tielsch, R.Varma, J.J Wang, N. Wang, S. West, L. Xu, M. Yasuda, X. Zhang, P. Mitchell, T.Y. Wong, ?Global Prevalence and Major Risk Factors of Diabetic Retinopathy? Diabetes Care Vol.35, issue 3 pp. 556-564, 2012 .
[3]. Q. Mohamed, C. M. Gillies, T.Y. Wong, ?Management of Diabetic Retinopathy: A Systematic Review?, JAMA Network, Vol. 29, issue 8, pp. 147-145, 2018.
[4]. N. Patton, T. M. Aslamc, M. MacGillivrayd, I. J. Dearye, B. Dhillonb, R. H. Eikelboomf, K. Yogesana and I. Constablea, ?Retinal image analysis: Concepts, applications and potential,? Retinal and Eye Research, Vol.25, pp. 99-127, 2006.
[5]. C. Lam, D. Yi, M. Guo, T. Lindsey ?Automated Detection of Diabetic Retinopathy using Deep Learning? Proceeding on AMIA joints Submits on Translational Science, pp.147-155, 2019.
[6]. K. J. Sayali, H.M. Baradkar ?Approach for Diabetic Retinopathy Analysis Using Artificial Neural Networks? International Research Journal of Engineering and Technology (IRJET), Vol.6, issue 2, pp. 2135-2139, 2019.
[7]. K. Verma, P. Deep, A.G. Ramakrishnan ?Detection and classification of diabetic retinopathy using retinal images? proceeding on India Conference 2011.
[8]. H. Pratt, F. Coenen, M. Deborah, S.P. Harding, Y. Zheng ?Convolutional Neural Networks for Diabetic Retinopathy? Procedia Computer. Vol.90, pp.200-205, 2016.
[9]. Z. Gao, J. Lie, J. Guo, Y. Chen, Z. Yi, J. Zhong ?Diagnosis of Diabetic Retinopathy Using Deep Neural Networks? IEEE Access. Vol.7, pp. 3360-3370, 2018.
[10]. T. Sajanna, K. Sai, G. Dinakar, H. Rajdeep ?Classifying Diabetic Retinopathy using Deep Learning Architecture? International Journal of Innovative Technology and Exploring Engineering (IJITEE) Vol.8, issue 6, pp. 1-6 2019.
[11]. L. Yunghui, N. yeh, S. Chen, Y. Chung ?Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network? Mobile Information System, pp. 1-14, 2019.