Diabetic Retinopathy Classification Using ResNet50 and VGG-16 Pretrained Networks

Authors

  • Mustapha AATILA LTI laboratory, ENSA, Chouaib Doukkali University, El Jadida 1166, Morocco
  • Mohamed LACHGAR LTI laboratory, ENSA, Chouaib Doukkali University, El Jadida 1166, Morocco https://orcid.org/0000-0002-6155-3309
  • Hamid HRIMECH University Hassan 1st, ENSA of Berrechid, LAMSAD, B.P 218, Morocco
  • Ali KARTIT LTI laboratory, ENSA, Chouaib Doukkali University, El Jadida 1166, Morocco

Keywords:

Diabetic retinopathy (DR), Deep learning, CNN, VGG-16, ResNet50

Abstract

Diabetic retinopathy (DR) is considered one of the worldwide diseases of blindness, especially in the elderly. The main reason for this disease is the complication of diabetes in the retinal blood vessels. Usually, the warning signs are not observed. Screening is an important key to diagnosing the early stages of diabetic retinopathy. This work represents an intelligent system of DR classification based on deep learning (DL) tools, especially convolutional neural networks (CNN). Proposed system can assist ophthalmologists to make a preliminary decision, it allows a DR classification considering normal eyes, mild DR, Moderate DR, Severe DR and Proliferative DR. Obtained results, in terms of classification accuracy, for DR classification using the color retinal background images based on VGG-16 and ResNet50 models are in order 70% and 25% respectively.

Downloads

Download data is not yet available.

References

Chan. J.C.Y, Chee. M.L, Tan. N.Y.Q. et al., “Differential effect of body mass index on the incidence of diabetes and diabetic retinopathy in two Asian populations,” Nutr & Diabetes, vol. 8, 2018.

Rishi P. Singh, Michael J. Elman, Simran K. Singh, Anne E. Fung and Ivaylo Stoilov, “Advances in the treatment of diabetic retinopathy,” Journal of Diabetes and its Complications, vol. 33, 2019.

Suvajit Dutta, Bonthala CS Manideep, Syed Muzamil Basha, Ronnie D. Caytiles and N. Ch. S. N. Iyenga, “Classification of Diabetic Retinopathy Images by Using Deep Learning Models,” International journal of Grid and Distributed Computing, vol 11, pp. 89-106, 2018.

Rahim S.S, Palade V, Almakky. I and Holzinger. A, “Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Deep Learning and Image Augmentation,” In: Holzinger A., Kieseberg P, Tjoa A., Weippl E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2019. Lecture Notes in Computer Science, vol 11713. Springer, Cham.

American Diabetes Association: Standards of medical care in diabetes—2009. Diabetes Care 32 (Suppl. 1): S13-S61, 2009

American Academy of Ophthalmology Retina Panel: Preferred practice pattern guidelines: diabetic retinopathy. San Francisco, American Academy of Ophthalmology, 2008. Available online from http://one.aao.org/CE/PracticeGuidelines/PPP_Content.aspx?cid=d0c 853d3-219f-487b-a524-326ab3cecd9a

Bruggeman. B, Zimmerman. C, LaPorte. A et al., “Barriers to retinopathy screening in youth and young adults with type 1 diabetes,” Pediatr Diabetes, p. 1– 5, 2020.

Klein. R, Klein. BE, Moss. SE, Davis. MD and DeMets. DL, “The Wisconsin Epidemiologic Study of Diabetic Retinopathy. III. Prevalence and risk of diabetic retinopathy when age at diagnosis is 30 or more years,” Arch Ophthalmol, vol. 102, p. 527-532, 1984.

Taylor. R and Batey. D, “Handbook of retinal screening in diabetes: diagnosis and management,” second ed. John Wiley & Sons, Ltd Wiley-Blackwell, 2012.

E. T. D. R. S. R. GROUP, “Grading diabetic retinopa thy from stereoscopic color fundus photographs- an extension of the modified Airlie House classification,” Ophthalmology, vol. 98, p. 786–806, 1991.

Scanlon. PH, Wilkinson. CP, Aldington. SJ and Matthews. DR, “A Practical manual of diabetic retinopathy management,” first ed. Wiley- Blackwell, 2009.

Dubow. M, et al., “Classification of human retinal microaneurysms using adaptive optics scanning light ophthalmoscope fluorescein angiography,” Investig Ophthalmol Vis Sci, vol. 55, p. 1299– 1309, 2014.

Ziemssen. F and Agostini. H. T, “Diabetic Retinopathy,” Essentials in Ophthalmology, p. 89–130, 2016.

Borsos. B et al., “Automatic detection of hard and soft exudates from retinal fundus images,” Acta Universitatis Sapientiae, Informatica, vol. 11, p. 65 – 79, 2019.

Gargeya. R. and Leng. T, “Automated Identification of Diabetic Retinopathy Using Deep Learning,” Ophthalmology, vol. 124, p. 962–969, 2017.

Ghosh. R, Ghosh. K and Maitra. S, “Automatic detection and classification of diabetic retinopathy stages using CNN,” 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2017, p. 550-554.

Saha. R, Chowdhury. A. R and Banerjee. S, “Diabetic Retinopathy Related Lesions Detection and Classification Using Machine Learning Technology,” Lecture Notes in Computer Science, 2016, p. 734–745.

Takahashi. H, Tampo. H, Arai. Y, Inoue. Y and Kawashima. H, “Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy,” PLoS ONE, vol. 12, 2017.

Amin. J, Sharif. M, Yasmin. M, Ali. H and Fernandes. S. L, “A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions,” Journal of Computational Science, vol. 19, p. 153–164, 2017.

Shankar. K, Perumal. E and Vidhyavathi. R.M, “Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images,” SN Appl. Sci., 2020.

Wang. X, Lu. Y, Wang. Y and Chen. WB, “Diabetic retinopathy stage classification using convolutional neural networks,” In: International Conference on information Reuse and Integration for data science, 2018. p. 465–71.

Nash. W, Drummond. T and Birbilis. N, “A review of deep learning in the study of materials degradation,” Npj Materials Degradation, vol. 2, 2018.

S. Jahromi, M. N., Buch-Cardona, P., Avots, E., Nasrollahi, K., Escalera, S., Moeslund, T. B., and Anbarjafari, G, “Privacy-Constrained Biometric System for Non-Cooperative Users,” Entropy, vol. 21, 2019.

Downloads

Published

2021-07-20

How to Cite

[1]
M. AATILA, M. . LACHGAR, H. HRIMECH, and A. KARTIT, “Diabetic Retinopathy Classification Using ResNet50 and VGG-16 Pretrained Networks”, IJCEDS, vol. 1, no. 1, pp. 1–7, Jul. 2021.

Issue

Vol. 1 No. 1 (2021): Volume 1. Issue 1

Section

Articles

License

Copyright (c) 2021 Mustapha AATILA, Mohamed LACHGAR, Hamid Hrimech, Ali Kartit

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright on any article published in the International Journal of Computer Engineering and Data Science (IJCEDS) is retained by the author(s). All articles are published under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits any non-commercial use, distribution, and reproduction in any medium, provided that the original work is properly cited.

License Agreement

By submitting and publishing their work in IJCEDS, the authors:

  • Grant IJCEDS the non-exclusive right to publish the article and to identify IJCEDS as the original publisher.

  • Authorize any third party to use, share, and reproduce the article for non-commercial purposes, provided that appropriate credit is given to the original authors and source, and a link to the license is included.