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Sadek, Noor Ali, Al-Dahan, Ziad Tarik, Rattan, Suzan Amana, Hussein, Ahmed F, Geraghty, Brendan, Kazaili, Ahmed. (1403). Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification. سامانه مدیریت نشریات علمی, (), -.
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, , , 1403, -.
Sadek, Noor Ali, Al-Dahan, Ziad Tarik, Rattan, Suzan Amana, Hussein, Ahmed F, Geraghty, Brendan, Kazaili, Ahmed. (1403). 'Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification', سامانه مدیریت نشریات علمی, (), pp. -.
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, 1403; (): -.

Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification

Journal of Biomedical Physics and Engineering
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 12 اسفند 1403    اصل مقاله (1.7 M)
نوع مقاله: Original Research
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.
کلیدواژه‌ها
Diabetic Retinopathy؛ Deep Learning؛ EyePACS؛ IDRiD؛ Iraqi Dataset؛ CNN؛ Decision Trees؛ Logistic Regression
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