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Cited by 303Retinal Imaging and AnalysisRetinal Diseases and TreatmentsAdvanced Neural Network Applications

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

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Alyoubi, Wejdan L., Abulkhair, Maysoon F., Shalash, Wafaa M.·2021·Sensors
DOI 10.3390/s21113704PMC PMC8198489
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Abstract

Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stopping or delaying the deterioration of sight, highlighting the importance of regular scanning using high-efficiency computer-based systems to diagnose cases early. The current work presented fully automatic diagnosis systems that exceed manual techniques to avoid misdiagnosis, reducing time, effort and cost. The proposed system classifies DR images into five stages—no-DR, mild, moderate, severe and proliferative DR—as well as localizing the affected lesions on retain surface. The system comprises two deep learning-based models. The first model (CNN512) used the whole image as an input to the CNN model to classify it into one of the five DR stages. It achieved an accuracy of 88.6% and 84.1% on the DDR and the APTOS Kaggle 2019 public datasets, respectively, compared to the state-of-the-art results. Simultaneously, the second model used an adopted YOLOv3 model to detect and localize the DR lesions, achieving a 0.216 mAP in lesion localization on the DDR dataset, which improves the current state-of-the-art results. Finally, both of the proposed structures, CNN512 and YOLOv3, were fused to classify DR images and localize DR lesions, obtaining an accuracy of 89% with 89% sensitivity, 97.3 specificity and that exceeds the current state-of-the-art results.

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  • This early detection is critical for preventing vision loss in patients suffering from diabetic retinopathy or other retinal diseases.

    “This early detection is critical for preventing vision loss in patients suffering from diabetic retinopathy or other retinal diseases [56,57].”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [56]

    The Introduction and Conclusions say that detecting diabetic retinopathy early helps manage its progression and reduce the risk of blindness. The cited paper does not extend that point to other retinal diseases, so that part of the claim is unsupported. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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  1. [2]Bourne R.R. Stevens G.A. White R.A. Smith J.L. Flaxman S.R. Price H. Jonas J.B. Keeffe J. Leasher J. Naidoo K. Causes of vision loss worldwide, 1990-2010: A systematic analysis Lancet Glob. Health 2013 1 339 349 10.1016/S2214-109X(13)70113-X 25104599 · DOI
  2. [3]Taylor R. Batey D. Handbook of Retinal Screening in Diabetes: Diagnosis and Management 2nd ed. John Wiley & Sons, Ltd., Wiley-Blackwell Hoboken, NJ, USA 2012 1 173 10.1002/0470028858 · DOI
  3. [4]Wilkinson C.P. Ferris F.L. Klein R.E. Lee P.P. Agardh C.D. Davis M. Dills D. Kampik A. Pararajasegaram R. Verdaguer J.T. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales Am. Acad. Ophthalmol. 2003 110 1677 1682 10.1016/S0161-6420(03)00475-5 13129861 · DOI
  4. [5]Deng L. Yu D. Deep learning: Methods and applications Found. Trends Signal Process. 2014 7 197 387 10.1561/2000000039 · DOI · Paper page
  5. [6]Vega R. Sanchez-Ante G. Falcon-Morales L.E. Sossa H. Guevara E. Retinal vessel extraction using lattice neural networks with dendritic processing Comput. Biol. Med. 2015 58 20 30 10.1016/j.compbiomed.2014.12.016 25589415 · DOI
  6. [7]Al Zaid E. Shalash W.M. Abulkhair M.F. Retinal blood vessels segmentation using Gabor filters Proceedings of the 2018 1st International Conference on Computer Applications & Information Security (ICCAIS) Riyadh, Saudi Arabia 4–6 April 2018 1 6
  7. [8]Sikder N. Masud M. Bairagi A.K. Arif A.S.M. Nahid A.A. Alhumyani H.A. Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images Symmetry 2021 13 670 10.3390/sym13040670 · DOI
  8. [9]Bakator M. Radosav D. Deep learning and medical diagnosis: A review of literature Multimodal Technol. Interact. 2018 2 47 10.3390/mti2030047 · DOI
  9. [10]Litjens G. Kooi T. Bejnordi B.E. Setio A.A.A. Ciompi F. Ghafoorian M. van der Laak J.A. van Ginneken B. Sánchez C.I. A survey on deep learning in medical image analysis Med. Image Anal. 2017 42 60 88 10.1016/j.media.2017.07.005 28778026 · DOI · Paper page
  10. [11]Lu L. Zheng Y. Carneiro G. Yang L. Deep Learning and Convolutional Neural Networks for Medical Image Computing Springer Berlin/Heidelberg, Germany 2017 10.1007/978-3-319-42999-1 · DOI
  11. [13]Redmon J. Farhadi A. YOLOv3: An Incremental Improvement arXiv 2018 1804.02767
  12. [15]Pires R. Avila S. Wainer J. Valle E. Abramoff M.D. Rocha A. A data-driven approach to referable diabetic retinopathy detection Artif. Intell. Med. 2019 96 93 106 10.1016/j.artmed.2019.03.009 31164214 · DOI
  13. [17]Decenciere E. Zhang X. Cazuguel G. Lay B. Cochener B. Trone C. Gain P. Ordonez R. Massin P. Erginay A. Feedback on a publicly distributed image database: The messidor database Image Anal. Stereol. 2014 33 231 234 10.5566/ias.1155 · DOI
  14. [18]Jiang H. Yang K. Gao M. Zhang D. Ma H. Qian W. An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Berlin, Germany 23–27 July 2019 2045 2048 10.1109/embc.2019.8857160 31946303 · DOI
  15. [20]Szegedy C. Vanhoucke V. Ioffe S. Shlens J. Wojna Z. Rethinking the Inception Architecture for Computer Vision Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, NV, USA 26 June–1 July 2016 2818 2826 10.1109/CVPR.2016.308 · DOI · Paper page
  16. [22]Liu Y.P. Li Z. Xu C. Li J. Liang R. Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network Artif. Intell. Med. 2019 99 101694 10.1016/j.artmed.2019.07.002 31606108 · DOI
  17. [23]Das S. Kharbanda K. Suchetha M. Raman R. Dhas E. Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy Biomed. Signal Process. Control 2021 68 102600 10.1016/j.bspc.2021.102600 · DOI
  18. [24]Wang X. Lu Y. Wang Y. Chen W.B. Diabetic retinopathy stage classification using convolutional neural networks Proceedings of the International Conference on Information Reuse and Integration for Data Science Salt Lake City, UT, USA 6–9 July 2018 465 471 10.1109/IRI.2018.00074 · DOI
  19. [25]Wan S. Liang Y. Zhang Y. Deep convolutional neural networks for diabetic retinopathy detection by image classification Comput. Electr. Eng. 2018 72 274 282 10.1016/j.compeleceng.2018.07.042 · DOI
  20. [26]Mobeen-Ur-Rehman Khan S.H. Abbas Z. Danish Rizvi S.M. Classification of Diabetic Retinopathy Images Based on Customised CNN Architecture Proceedings of the 2019 Amity International Conference on Artificial Intelligence, AICAI 2019 Dubai, United Arab Emirates 4–6 February 2019 244 248 10.1109/AICAI.2019.8701231 · DOI
  21. [27]Zhang W. Zhong J. Yang S. Gao Z. Hu J. Chen Y. Yi Z. Automated identification and grading system of diabetic retinopathy using deep neural networks Knowl. Based Syst. 2019 175 12 25 10.1016/j.knosys.2019.03.016 · DOI
  22. [28]Harangi B. Toth J. Baran A. Hajdu A. Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Berlin, Germany 23–27 July 2019 2699 2702 10.1109/embc.2019.8857073 31946452 · DOI
  23. [29]Shanthi T. Sabeenian R.S. Modified Alexnet architecture for classification of diabetic retinopathy images Comput. Electr. Eng. 2019 76 56 64 10.1016/j.compeleceng.2019.03.004 · DOI
  24. [30]Li X. Hu X. Yu L. Zhu L. Fu C.W. Heng P.A. CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading IEEE Trans. Med Imaging 2020 39 1483 1493 10.1109/TMI.2019.2951844 31714219 · DOI
  25. [31]Dekhil O. Naglah A. Shaban M. Ghazal M. Taher F. Elbaz A. Deep Learning Based Method for Computer Aided Diagnosis of Diabetic Retinopathy Proceedings of the IST 2019—IEEE International Conference on Imaging Systems and Techniques Abu Dhabi, United Arab Emirates 9–10 December 2019 1 4 10.1109/IST48021.2019.9010333 · DOI
  26. [32]He A. Li T. Li N. Wang K. Fu H. CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading IEEE Trans. Med. Imaging 2020 40 143 153 10.1109/TMI.2020.3023463 32915731 · DOI
  27. [33]Kassani S.H. Kassani P.H. Khazaeinezhad R. Wesolowski M.J. Schneider K.A. Deters R. Diabetic retinopathy classification using a modified xception architecture Proceedings of the 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Ajman, United Arab Emirates 10–12 December 2019 1 6
  28. [34]Bodapati J.D. Shaik N.S. Naralasetti V. Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification J. Ambient. Intell. Humaniz. Comput. 2021 1 15 10.1007/s12652-020-02727-z · DOI
  29. [35]Hsieh Y.T. Chuang L.M. Jiang Y.D. Chang T.J. Yang C.M. Yang C.H. Chan L.W. Kao T.Y. Chen T.C. Lin H.C. Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening J. Formos. Med Assoc. 2021 120 165 171 10.1016/j.jfma.2020.03.024 32307321 · DOI
  30. [36]Zago G.T. Andreão R.V. Dorizzi B. Teatini Salles E.O. Diabetic retinopathy detection using red lesion localization and convolutional neural networks Comput. Biol. Med. 2019 116 103537 10.1016/j.compbiomed.2019.103537 31747632 · DOI
  31. [37]Li T. Gao Y. Wang K. Guo S. Liu H. Kang H. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening Inf. Sci. 2019 501 511 522 10.1016/j.ins.2019.06.011 · DOI
  32. [38]Wang J. Luo J. Liu B. Feng R. Lu L. Zou H. Automated diabetic retinopathy grading and lesion detection based on the modified R-FCN object-detection algorithm IET Comput. Vis. 2020 14 1 8 10.1049/iet-cvi.2018.5508 · DOI
  33. [40]Zisserman K.S. Zisserman A. Very deep convolutional networks for large-scale image recognition Proceedings of the International Conference on Learning Representations San Diego, CA, USA 7–9 May 2015
  34. [41]Szegedy C. Liu W. Jia Y. Sermanet P. Reed S. Anguelov D. Erhan D. Vanhoucke V. Rabinovich A. Going Deeper with Convolutions Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston, MA, USA 7–12 June 2015 1 9 10.1109/CVPR.2015.7298594 · DOI · Paper page
  35. [42]He K. Zhang X. Ren S. Sun J. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition IEEE Trans. Pattern Anal. Mach. Intell. 2015 37 1904 1916 10.1109/TPAMI.2015.2389824 26353135 · DOI · Paper page
  36. [44]Huang G. Liu Z. van der Maaten L. Weinberger K.Q. Densely Connected Convolutional Networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Honolulu, HI, USA 21–26 July 2017 4700 4708
  37. [45]Porwal P. Pachade S. Kamble R. Kokare M. Deshmukh G. Sahasrabuddhe V. Meriaudeau F. Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research Data 2018 3 25 10.3390/data3030025 · DOI
  38. [47]Alyoubi W.L. Shalash W.M. Abulkhair M.F. Diabetic Retinopathy Detection through Deep Learning Technique: A Review Inform. Med. Unlocked 2020 20 1 11 10.1016/j.imu.2020.100377 · DOI
  39. [48]Hu J. Shen L. Sun G. Squeeze-and-excitation networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Salt Lake City, UT, USA 18–23 June 2018 7132 7141
  40. [50]Dai J. Li Y. He K. Sun J. R-FCN: Object detection via region-based fully convolutional networks arXiv 2016 1605.06409
  41. [51]Li T. Bo W. Hu C. Kang H. Liu H. Wang K. Fu H. Applications of Deep Learning in Fundus Images: A Review Med. Image Anal. 2021 69 101971 10.1016/j.media.2021.101971 33524824 · DOI
  42. [53]Dutta S. Manideep B.C. Basha S.M. Caytiles R.D. Iyengar N.C.S.N. Classification of Diabetic Retinopathy Images by Using Deep Learning Models Int. J. Grid Distrib. Comput. 2018 11 99 106 10.14257/ijgdc.2018.11.1.09 · DOI
  43. [54]Zhou M. Jin K. Wang S. Ye J. Qian D. Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment IEEE Trans. Biomed. Eng. 2018 65 521 527 10.1109/TBME.2017.2700627 28475043 · DOI
  44. [55]Pisano E.D. Zong S. Hemminger B.M. Deluca M. Johnston R.E. Muller K. Braeuning M.P. Pizer S.M. Contrast Limited Adaptive Histogram Equalization Image Processing to Improve the Detection of Simulated Spiculations in Dense Mammograms J. Digit. Imaging 1998 11 193 200 10.1587/transinf.2016EDL8049 9848052 PMC3453156 · DOI
  45. [56]Zuiderveld K. Contrast Limited Adaptive Histogram Equalization Graph. Gems IV 1994 474 485 10.1016/b978-0-12-336156-1.50061-6 · DOI · Paper page
  46. [57]Sonali Sahu S. Singh A.K. Ghrera S. Elhoseny M. An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE Optics Laser Technol. 2019 110 87 98 10.1016/j.optlastec.2018.06.061 · DOI
  47. [58]Pratt H. Coenen F. Broadbent D.M. Harding S.P. Zheng Y. Convolutional Neural Networks for Diabetic Retinopathy Procedia Comput. Sci. 2016 90 200 205 10.1016/j.procs.2016.07.014 · DOI
  48. [59]Ketkar N. Deep Learning with Python Springer Berlin/Heidelberg, Germany 2017 1 235 10.1007/978-1-4842-2766-4 · DOI
  49. [60]Shalash W.M. Driver Fatigue Detection with Single EEG Channel Using Transfer Learning Proceedings of the 2019 IEEE International Conference on Imaging Systems and Techniques (IST) Abu Dhabi, United Arab Emirates 9–10 December 2019
  50. [61]COVER T. HART P. Nearest Neighbor Pattern Classfication IEEE Trans. Inf. Theory 1967 13 21 27 10.1109/TIT.1967.1053964 · DOI · Paper page
  51. [63]Lee W.Y. Park S.M. Sim K.B. Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm Optik 2018 172 359 367 10.1016/j.ijleo.2018.07.044 · DOI
  52. [65]Huang Q. Mao J. Liu Y. An improved grid search algorithm of SVR parameters optimization Proceedings of the 2012 IEEE 14th International Conference on Communication Technology Chengdu, China 9–11 November 2012 1022 1026
  53. [66]Maclaurin D. Duvenaud D. Adams R. Gradient-based hyperparameter optimization through reversible learning Proceedings of the International Conference on Machine Learning, PMLR Lille, France 7–9 July 2015 2113 2122
  54. [67]Smith L.N. Cyclical learning rates for training neural networks Proceedings of the IEEE Winter Conference on Applications of Computer Vision, WACV 2017 Santa Rosa, CA, USA 24–31 March 2017 464 472 10.1109/WACV.2017.58 · DOI
  55. [68]Ahn S. Pham Q. Shin J. Song S.J. Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability Electronics 2021 10 726 10.3390/electronics10060726 · DOI
  56. [69]Anton N. Dragoi E.N. Tarcoveanu F. Ciuntu R.E. Lisa C. Curteanu S. Doroftei B. Ciuntu B.M. Chiseliţă D. Bogdănici C.M. Assessing Changes in Diabetic Retinopathy Caused by Diabetes Mellitus and Glaucoma Using Support Vector Machines in Combination with Differential Evolution Algorithm Appl. Sci. 2021 11 3944 10.3390/app11093944 · DOI

Cited by

Sorted by citations

Authors
Alyoubi, W. L., Abulkhair, M. F., & Shalash, W. M.
Affiliations
King Abdulaziz University (SA)
Corresponding author
Alyoubi, Wejdan L.
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2021-05-26
Volume
21
Issue
11
Pages
3704
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s21113704
PMC
PMC8198489
OpenAlex
W3165183789
Cited by
303
References
6
Citations by year
2026: 42 · 2025: 71 · 2024: 69 · 2023: 69 · 2022: 49 · 2021: 2
FWCI
24.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
  • Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
diabetic retinopathy, retinal image classification, lesion localization, YOLOv3, convolutional neural networks, automated diagnosis, APTOS dataset, model fusion
MeSH terms
Deep Learning, Diabetes Mellitus, Diabetic Retinopathy, Fundus Oculi, Humans, Retinal Vessels

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