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Sadek, Noor Ali, Al-Dahan, Ziad Tarik, Rattan, Suzan Amana, Hussein, Ahmed F, Geraghty, Brendan, Kazaili, Ahmed. (1405). Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification. سامانه مدیریت نشریات علمی, 16(3), 191-204. doi: 10.31661/jbpe.v0i0.2406-1774
Noor Ali Sadek; Ziad Tarik Al-Dahan; Suzan Amana Rattan; Ahmed F Hussein; Brendan Geraghty; Ahmed Kazaili. "Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification". سامانه مدیریت نشریات علمی, 16, 3, 1405, 191-204. doi: 10.31661/jbpe.v0i0.2406-1774
Sadek, Noor Ali, Al-Dahan, Ziad Tarik, Rattan, Suzan Amana, Hussein, Ahmed F, Geraghty, Brendan, Kazaili, Ahmed. (1405). 'Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification', سامانه مدیریت نشریات علمی, 16(3), pp. 191-204. doi: 10.31661/jbpe.v0i0.2406-1774
Sadek, Noor Ali, Al-Dahan, Ziad Tarik, Rattan, Suzan Amana, Hussein, Ahmed F, Geraghty, Brendan, Kazaili, Ahmed. Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification. سامانه مدیریت نشریات علمی, 1405; 16(3): 191-204. doi: 10.31661/jbpe.v0i0.2406-1774

Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification

Journal of Biomedical Physics and Engineering
دوره 16، شماره 3 - شماره پیاپی 1، شهریور 2026، صفحه 191-204    اصل مقاله (1.75 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2406-1774
نویسندگان
Noor Ali Sadek* 1؛ Ziad Tarik Al-Dahan2؛ Suzan Amana Rattan3؛ Ahmed F Hussein4؛ Brendan Geraghty5؛ Ahmed Kazaili6
1Department of Biomedical Engineering, Al-Nahrain University, Baghdad, Iraq
2Department of Biomedical Engineering, College of Engineering, Al-Bayan University, Baghdad, Iraq
3AlKindy College of Medicine, University of Baghdad, Baghdad, Iraq
4Artificial Intelligent and Robotics Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq
5Department of Musculoskeletal and Ageing Science, Institute of Life Course and Medical Sciences, University of Liverpool, L7 8TX, UK
6School of Engineering, University of Liverpool, Liverpool, L69 3GH, UK
چکیده
Background: Diabetic Retinopathy (DR) is one of several retinal microvascular complications of Diabetes Mellitus (DM), a disease of increasing global prevalence. However, early detection and treatment can reduce or even prevent DR progression. In this work, Deep Learning (DL) techniques are used to grade DR from an early stage using either binary or multiclass classification as a clinical aid to help reduce the risk of patient vision loss.
Objective: The primary objective of this research is to develop a low-cost, fast, and accurate automated system using DL for the early detection and classification of DR from retina fundus images.
Material and Methods: This cross-sectional study employed three DL models, namely Convolutional Neural Networks (CNNs), decision tree, and logistic regression, to categorize three distinct clinically graded datasets, namely the Iraqi dataset, the Indian Diabetic Retinopathy Image Dataset (IDRiD) and the Eyepacs dataset, according to DR severity.
Results: Evaluation of the DL model results showed that logistic regression emerged as the most effective, where accuracies of 99%, 99.3%, and 99.4% were achieved for the Iraqi, IDRiD, and Eyepacs datasets, respectively. Conversely, the decision-tree model achieved the lowest accuracy across the three datasets with 95.2%, 95.9%, and 96.0%, respectively. 
Conclusion: The logistic regression model demonstrated the highest overall accuracy of the three models for the classification of DR, with the Iraqi dataset with the highest accuracy of the three datasets.

تازه های تحقیق

Noor Ali Sadek (Google Scholar)

کلیدواژه‌ها
Diabetic Retinopathy؛ Deep Learning؛ EyePACS؛ IDRiD؛ Iraqi Dataset؛ CNN؛ Decision Trees؛ Logistic Regression
مراجع
  1. Reguant R, Brunak S, Saha S. Understanding inherent image features in CNN-based assessment of diabetic retinopathy. Sci Rep. 2021;11(1):9704. doi: 10.1038/s41598-021-89225-0. PubMed PMID: 33958686. PubMed PMCID: PMC8102512.
  2. Aguirre F, Brown A, Cho NH, Dahlquist G, Dodd S, Dunning T, et al. IDF Diabetes Atlas. 6th ed. Basel, Switzerland: International Diabetes Federation; 2013.
  3. Ting DS, Tan KA, Phua V, Tan GS, Wong CW, Wong TY. Biomarkers of diabetic retinopathy. Curr Diab Rep. 2016;16:125. doi: 10.1007/s11892-016-0812-9.
  4. Bourne RR, Stevens GA, White RA, Smith JL, Flaxman SR, Price H, et al. Causes of vision loss worldwide, 1990-2010: a systematic analysis. Lancet Glob Health. 2013;1(6):e339-49. doi: 10.1016/S2214-109X(13)70113-X. PubMed PMID: 25104599.
  5. Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. 2010;376(9735):124-36. doi: 10.1016/S0140-6736(09)62124-3. PubMed PMID: 20580421.
  6. Kroenke K. Telemedicine screening for eye disease. JAMA. 2015;313(16):1666-7. doi: 10.1001/jama.2015.107. PubMed PMID: 25919530.
  7. Stolte S, Fang R. A survey on medical image analysis in diabetic retinopathy. Med Image Anal. 2020;64:101742. doi: 10.1016/j.me2020.101742. PubMed PMID: 32540699.
  8. Maswood MM, Hussain T, Khan MB, Islam MT, Alharbi AG. Cnn based detection of the severity of diabetic retinopathy from the fundus photography using efficientnet-b5. In 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON); Vancouver, BC, Canada: IEEE ;2020. p. 0147-50.
  9. Yung-Hui L, Nai-Ning Y, Purwandari K, Harfiya LN. Clinically applicable deep learning for diagnosis of diabetic retinopathy. In: 2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media); Bali, Indonesia: IEEE: 2019. p. 124-9.
  10. Paranjpe MJ, Kakatkar MN. Automated diabetic retinopathy severity classification using support vector machine. Int J Res Sci Adv Technol. 2013;3(3):86-91.
  11. Liaw A. Classification and regression by randomForest. R News. 2002;2:18-22.
  12. Lim G, Lee ML, Hsu W, Wong TY. Transformed representations for convolutional neural networks in diabetic retinopathy screening. In: Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence; Canada: 2014.
  13. Bindu CH, Sravanthi GS. Retinopathy detection of EYE images in diabetic patients. Indian J Sci Technol. 2019;12(3):1-7. doi: 10.17485/ijst/2018/v12i3/141597.
  14. Priya Henry AG, Jude A. Convolutional neural-network-based classification of retinal images with different combinations of filtering techniques. Open Computer Science. 2021;11(1):480-90. doi: 10.1515/comp-2020-0177.
  15. Levi G, Hassner T. Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); Boston, MA, USA: IEEE; 2015. p. 34-42.
  16. Salma A, Bustamam A, Sarwinda D. Diabetic Retinopathy Detection Using GoogleNet Architecture of Convolutional Neural Network Through Fundus Images. NST Proceeding. 2021:1-6. doi: 10.11594/nstp.2021.0701.
  17. Lam C, Yi D, Guo M, Lindsey T. Automated Detection of Diabetic Retinopathy using Deep Learning. AMIA Jt Summits Transl Sci Proc. 2018;2017:147-55. PubMed PMID: 29888061. PubMed PMCID: PMC5961805.
  18. Butt MM, Iskandar DNFA, Abdelhamid SE, Latif G, Alghazo R. Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features. Diagnostics (Basel). 2022;12(7):1607. doi: 10.3390/diagnostics12071607. PubMed PMID: 35885512. PubMed PMCID: PMC9324358.
  19. Kang EY, Yeung L, Lee YL, Wu CH, Peng SY, Chen YP, et al. A Multimodal Imaging-Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study. JMIR Med Inform. 2021;9(5):e28868. doi: 10.2196/28868. PubMed PMID: 34057419. PubMed PMCID: PMC8204240.
  20. Adem K. Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks. Expert Systems with Applications. 2018;114:289-95. doi: 10.1016/j.eswa.2018.07.053.
  21. Xu K, Feng D, Mi H. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. 2017;22(12):2054. doi: 10.3390/molecules22122054. PubMed PMID: 29168750. PubMed PMCID: PMC6149821.
  22. Zhu C, Idemudia CU, Feng W. Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques. Inform Med Unlocked. 2019;17:100179. doi: 10.1016/j.imu.2019.100179.
  23. Karami A, Askarishahi M, Namiranian N. Comparison of Different Decision Tree Algorithms for Classification of Retinopathy Patients in Yazd City, Central Part of Iran. Journal of Community Health Research. 2022;11(3):158-64. doi: 10.18502/jchr.v11i3.10867.
  24. Cuadros J, Bresnick G. EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. J Diabetes Sci Technol. 2009;3(3):509-16. doi: 10.1177/193229680900300315. PubMed PMID: 20144289. PubMed PMCID: PMC2769884.
  25. Prasanna P, Samiksha P, Ravi K, Manesh K, Girish D, Vivek S, Fabrice M. Indian diabetic retinopathy image dataset (idrid). IEEE Dataport; 2018. Available from: https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid.
  26. Geraghty B, Kazaili A, Sadek NA, Al-Dahan ZT, Rattan SA, Faeq Hussein AF. Iraqi Retinal Fundus Diabetic Retinopathy Dataset (IRFDRD). 6th International Scientific Conference titled “Modern Science and Technology: Foundations for Ensuring Community Development and Energy Utilization’’; Duhok, Iraq: 2024. Available from: https://github.com/noor-aliz810/IRFDRD.git.
  27. Maalouf M. Logistic regression in data analysis: an overview. Int J Data Analysis Techniques and Strategies. 2011;3(3):281-99. doi: 10.1504/IJDATS.2011.041335.
  28. Xin Z, Hua L, Wang XH, Zhao D, Yu CG, Ma YH, et al. Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals. Int J Endocrinol. 2017;2017:3894870. doi: 10.1155/2017/3894870. PubMed PMID: 28638408. PubMed PMCID: PMC5468553.
  29. Chang V, Bailey J, Xu QA, Sun Z. Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms. Neural Comput Appl. 2022:1-17. doi: 10.1007/s00521-022-07049-z. PubMed PMID: 35345556. PubMed PMCID: PMC8943493.
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