Document Type : Original Research

Authors

• Noor Ali Sadek ¹
• Ziad Tarik Al-Dahan ²
• Suzan Amana Rattan ³
• Ahmed F Hussein ⁴
• Brendan Geraghty ⁵
• Ahmed Kazaili ⁶

¹ Department of Biomedical Engineering, Al-Nahrain University, Baghdad, Iraq

² Department of Biomedical Engineering, College of Engineering, Al-Bayan University, Baghdad, Iraq

³ AlKindy College of Medicine, University of Baghdad, Baghdad, Iraq

⁴ Artificial Intelligent and Robotics Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq

⁵ Department of Musculoskeletal and Ageing Science, Institute of Life Course and Medical Sciences, University of Liverpool, L7 8TX, UK

⁶ School of Engineering, University of Liverpool, Liverpool, L69 3GH, UK

Abstract

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.

Keywords

• Diabetic Retinopathy
• Deep Learning
• EyePACS
• IDRiD
• Iraqi Dataset
• CNN
• Decision Trees
• Logistic Regression