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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Deep learning in the grading of diabetic retinopathy: A review
Nurul Mirza Afiqah Tajudin, Kuryati Kipli, Muhammad Hamdi Mahmood, et al.
IET Computer Vision (2022) Vol. 16, Iss. 8, pp. 667-682
Open Access | Times Cited: 17

Showing 17 citing articles:

Deep Learning in Automatic Diabetic Retinopathy Detection and Grading Systems: A Comprehensive Survey and Comparison of Methods
Israa Y. AbuShawish, Sudipta Modak, Esam Abdel‐Raheem, et al.
IEEE Access (2024) Vol. 12, pp. 84785-84802
Open Access | Times Cited: 8
A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images
Dasari Bhulakshmi, Dharmendra Singh Rajput
PeerJ Computer Science (2024) Vol. 10, pp. e1947-e1947
Open Access | Times Cited: 6
Classification of Diabetic Retinopathy Images through Deep Learning Models - Color Channel Approach: A Review
Sardar Salih, Adnan Mohsin Abdulazeez
Indonesian Journal of Computer Science (2024) Vol. 13, Iss. 1
Open Access | Times Cited: 5
Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy
Minyar Sassi Hidri, Adel Hidri, Suleiman Ali Alsaif, et al.
Information (2025) Vol. 16, Iss. 3, pp. 221-221
Open Access
A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet
Midhula Vijayan, S Venkatakrishnan
Diagnostics (2023) Vol. 13, Iss. 4, pp. 774-774
Open Access | Times Cited: 15
Convolution Neural Networks for Disease Prediction: Applications and Challenges
Snowber Mushtaq, Omkar Singh
Scalable Computing Practice and Experience (2024) Vol. 25, Iss. 1, pp. 615-636
Open Access | Times Cited: 4
Advancing diabetic retinopathy classification using ensemble deep learning approaches
Ankur Biswas, Rita Banik
Biomedical Signal Processing and Control (2025) Vol. 106, pp. 107804-107804
Closed Access
Computer-Aided Diagnosis-Based Grading Classification of Diabetic Retinopathy Using Deep Graph Correlation Network with IRF
Venkata Kotam Raju Poranki, B. Srinivasarao
SN Computer Science (2024) Vol. 5, Iss. 2
Closed Access | Times Cited: 3
Explainable Artificial Intelligence in Deep Learning Neural Nets-Based Digital Images Analysis
Alexey N. Averkin, Egor N. Volkov, Sergey Yarushev
Journal of Computer and Systems Sciences International (2024) Vol. 63, Iss. 1, pp. 175-203
Closed Access | Times Cited: 1
Explainable artificial intelligence in deep learning neural nets-based digital images analysis
Alexey N. Averkin, Egor N. Volkov, Sergey Yarushev
Известия Российской академии наук Теория и системы управления (2024), Iss. 1, pp. 150-178
Closed Access | Times Cited: 1
Enhanced Detection of Diabetic Retinopathy Using Ensemble Machine Learning: A Comparative Study
Sanjay Tanaji Sanamdikar, Satish Akaram Patil, D Patil, et al.
Ingénierie des systèmes d information (2023) Vol. 28, Iss. 6, pp. 1663-1668
Open Access | Times Cited: 3
Estimating Risk Levels and Epidemiology of Diabetic Retinopathy using Transfer Learning
Ankur Biswas, Rita Banik
(2023), pp. 287-292
Closed Access | Times Cited: 2
Deep learning ensemble framework for multiclass diabetic retinopathy classification
Mudit Saxena, Pratap Narra, Mayank Saxena, et al.
TELKOMNIKA (Telecommunication Computing Electronics and Control) (2024) Vol. 22, Iss. 3, pp. 665-665
Open Access
Detection and classification of diabetic retinopathy based on ensemble learning
Ankur Biswas, Rita Banik
Advances in Computational Intelligence (2024) Vol. 4, Iss. 3
Closed Access
Computationally efficient deep learning models for diabetic retinopathy detection: a systematic literature review
Nazeef Ul Haq, Talha Waheed, Kashif Ishaq, et al.
Artificial Intelligence Review (2024) Vol. 57, Iss. 11
Open Access
DRNET: Improved Deep Learning Solution to detect Diabetic Retinopathy by incorporating Explainability, Uncertainty and Generalisation
Mudit Saxena, Pratap Narra, Rakhi Saxena
Research Square (Research Square) (2022)
Open Access | Times Cited: 1
Improving Safe Driving with Diabetic Retinopathy Detection
Niusha Sangsefidi, Saeed Sharifian
Communications in computer and information science (2023), pp. 53-61
Closed Access
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