Detection of Diabetic Retinopathy Based on Convolutional Neural Networks: A Review
Halbast Rashid Ismael, Adnan Mohsin Abdulazeez, Dathar Abas Hasan
Asian Journal of Research in Computer Science · pp. 1–15 · Published 8 May 2021
10.9734/ajrcos/2021/v8i330200Abstract
A major cause of human vision loss worldwide is Diabetic retinopathy (DR). The disease requires early screening for slowing down the progress. However, in low-resource settings where few ophthalmologists are available to care for all patients with diabetes, the clinical diagnosis of DR will be a considerable challenge. This paper, review the most recent studies on the detection of DR by using one of the efficient algorithms of deep learning, which is Convolutional Neural Networks (CNN), which highly used to detect DR features from retinal images. CNNs approach to DR detection saves time and expense, and is more efficient and accurate than manual diagnostics. Therefore, CNN is essential and beneficial for DR detection.
References (32)
- 1 ImageNet classification with deep convolutional neural networks [DOI]
- 2 Deep Learning
- 3 Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs [DOI]
- 4 Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response [DOI]
- 5 Automated Identification of Diabetic Retinopathy Using Deep Learning [DOI]
- 6 Convolutional Neural Networks for Diabetic Retinopathy [DOI]
- 7 Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research [DOI]
- 8 the DIARETDB1 diabetic retinopathy database and evaluation protocol [DOI]
- 9 Robust Vessel Segmentation in Fundus Images [DOI]
- 10 Deep convolutional neural networks for diabetic retinopathy detection by image classification [DOI]
- 11 Deep neural networks to predict diabetic retinopathy [DOI]
- 12 Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image [DOI]
- 13 Diabetic retinopathy detection using red lesion localization and convolutional neural networks [DOI]
- 14 Diagnosis of Diabetic Retinopathy Using Deep Neural Networks [DOI]
- 15 Microaneurysm detection using fully convolutional neural networks [DOI]
- 16 Anatomy-specific classification of medical images using deep convolutional nets [DOI]
- 17 A data-driven approach to referable diabetic retinopathy detection [DOI]
- 18 Gene Selection and Classification of Microarray Data Using Convolutional Neural Network [DOI]
- 19 Machine learning and Region Growing for Breast Cancer Segmentation [DOI]
- 20 Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy [DOI]
- 21 Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks [DOI]
- 22 Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network [DOI]
- 23 Optomap ultrawide field imaging identifies additional retinal abnormalities in patients with diabetic retinopathy [DOI]
- 24 An Approach to Detecting Diabetic Retinopathy Based on Integrated Shallow Convolutional Neural Networks [DOI]
- 25 Automatic detection of non-proliferative diabetic retinopathy in retinal fundus images using convolution neural network [DOI]
- 26 Diabetic Retinopathy Detection Based on Deep Convolutional Neural Networks [DOI]
- 27 A multi-view deep learning architecture for classification of breast microcalcifications [DOI]
- 28 Significant features for steganography techniques using deoxyribonucleic acid: a review [DOI]
- 29 Modality Classification for Medical Images Using Multiple Deep Convolutional Neural Networks [DOI]
- 30 Detection of Diabetic Retinopathy Using Pretrained Deep Neural Networks [DOI]
- 31 On Deep Learning based algorithms for Detection of Diabetic Retinopathy [DOI]
- 32 Going Deeper with Convolutions [DOI]
Cited by 16
Kavita Lal, M. Deepika, Aenugu Shivajyothi · 2026 4th International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) · 2026
S. Sahoo, Rekha Sahu · 2025 2nd International Conference on Circuits, Power and Intelligent Systems (CCPIS) · 2025
Minyar Sassi Hidri, Adel Hidri, S. Alsaif · Inf. · 2025
Shruti Jalapur, Nagamma Patil · SN Computer Science · 2025
Masoud Muhammed Hassan, Halbast Rashid Ismail · Heliyon · 2025
Anis-Ul-Islam Rafid, S. Sanjana, Muhaimin Bin Munir · Signal, Image and Video Processing · 2023
R. YUVARAJ · International Journal of Engineering Technology and Management Sciences · 2022
Diyar Qader Zeebaree, A. Abdulazeez, Lozan M. Abdullrhman · Asian Journal of Research in Computer Science · 2021
Veeranna Moodu, Rajendra Prasad Chintha, Shashank Rebelli · 2025
Qaddour Mariem, Yassine Benjelloune Touimi, Khalid Minaoui · Sustainable Artificial Intelligence-Powered Applications · 2025
Showing 10 of 16 known citations — external sources report more than can currently be individually listed.
Related research
- Association of MnSOD-47C/TGENE Polymorphism (Rs 4880-47C/T) in Sudanese Patient with Diabetic Retinopathy — shares topic coverage
- Comparison of Choroidal Thickness in Normal Subjects and Patients with Diabetes — shares topic coverage
- Hard Limiting and Soft Computing Techniques for Detection of Exudates in Retinal Images: A Futuristic Review — shares topic coverage
- An Automated Mechanism for Early Screening and Diagnosis of Diabetic Retinopathy in Human Retinal Images — shares topic coverage
- Comparison of Canny’s and Snakes’ Algorithm as Applied to Diabetic Retinopathy — shares topic coverage
Article metrics
Real usage data collected on this platform.
29
Page views
0
PDF downloads
3
Outbound clicks
16
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.