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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:

Transfer Learning for Diabetic Retinopathy Detection: A Study of Dataset Combination and Model Performance
A. M. Mutawa, Shahad Alnajdi, Sai Sruthi
Applied Sciences (2023) Vol. 13, Iss. 9, pp. 5685-5685
Open Access | Times Cited: 19

Showing 19 citing articles:

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches
Muhammad Usman Akram, Muhammad Adnan, Syed Farooq Ali, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Diabetic Retinopathy Detection and Grading: A Transfer Learning Approach Using Simultaneous Parameter Optimization and Feature-Weighted ECOC Ensemble
W. K. Wong, Filbert H. Juwono, Catur Apriono
IEEE Access (2023) Vol. 11, pp. 83004-83016
Open Access | Times Cited: 28
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 multi-resolution convolutional attention network for efficient diabetic retinopathy classification
Sandeep Madarapu, Samit Ari, Kamalakanta Mahapatra
Computers & Electrical Engineering (2024) Vol. 117, pp. 109243-109243
Closed Access | Times Cited: 5
A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform
A. M. Mutawa, Khalid Al-Sabti, Seemant Raizada, et al.
Applied Sciences (2024) Vol. 14, Iss. 11, pp. 4428-4428
Open Access | Times Cited: 5
A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform
A. M. Mutawa, Khalid Al-Sabti, Seemant Raizada, et al.
(2024)
Open Access | Times Cited: 3
Predicting diabetic retinopathy stage using Siamese Convolutional Neural Network
Maria Santos, Carlos Valadão, Cassius Zanetti Resende, et al.
Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization (2024) Vol. 12, Iss. 1
Open Access | Times Cited: 2
Deciphering the impact of diversity in CNN-based ensembles on overcoming data imbalance and scarcity in medical datasets: A case study on diabetic retinopathy
Inamullah, Saima Hassan, Samir Brahim Belhaouari, et al.
Informatics in Medicine Unlocked (2024) Vol. 49, pp. 101557-101557
Open Access | Times Cited: 2
Advanced Detection of Diabetic Retinopathy: Employing Hybrid Deep Neural Networks and Multi-Scale Image Analysis Techniques
A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, et al.
(2024)
Open Access | Times Cited: 1
Weighted Voting Ensemble Learning of CNN Architectures for Diabetic Retinopathy Classification
Anita Desiani, Rifkie Primartha, Herlina Hanum, et al.
JURNAL INFOTEL (2024) Vol. 16, Iss. 1
Open Access
Towards improved fundus disease detection using Swin Transformers
M. Abdul Jawad, Farida Khursheed, Shah Nawaz, et al.
Multimedia Tools and Applications (2024) Vol. 83, Iss. 32, pp. 78125-78159
Closed Access
DIABETIC RETINOPATHY SEVERITY CLASSIFICATION USING CUSTOMIZED CNN
Pradnya S. Kulkarni, Shivani Patil
Biomedical Engineering Applications Basis and Communications (2024) Vol. 36, Iss. 03
Closed Access
FEATURES OF THE MODIFICATION OF THE INCEPTIONRESNETV2 ARCHITECTURE AND THE CREATION OF A DIAGNOSTIC SYSTEM FOR DETERMINING THE DEGREE OF DAMAGE TO RETINAL VESSELS
Dmytro Prochukhan
Computer Systems and Information Technologies (2024), Iss. 1, pp. 27-32
Open Access
Deciphering the Impact of Diversity in Cnn-Based Ensembles Onovercoming Data Imbalance and Scarcity in Medical Datasets: Acase Study on Diabetic Retinopathyde
Samir Brahim Belhaouari, Inamullah, Saima Hassan, et al.
(2024)
Closed Access
Predicting Diabetic Retinopathy Severity with Deep Learning: A Survey of Fundus Image Analysis Technique
A Binusha Sornil, C. Sheeja Herobin Rani, I.Rexilin Sheeba
(2024)
Closed Access
Data-driven 2D-EWT based diabetic retinopathy identification using hybrid neural network
Amit Rawat, Maheshwari Prasad Singh, Rishi Raj Sharma
Image and Vision Computing (2024) Vol. 150, pp. 105194-105194
Closed Access
Convolutional Neural Network Model for Diabetic Retinopathy Feature Extraction and Classification
Sharan Subramanian, Leilani H. Gilpin
Journal of Student Research (2024) Vol. 13, Iss. 1
Open Access
MobileNet-V2 /IFHO model for Accurate Detection of early-stage diabetic retinopathy
Chunjuan Huang, Mohammad Reza Mohammad Taghizadeh Sarabi, Adham E Ragab
Heliyon (2024) Vol. 10, Iss. 17, pp. e37293-e37293
Open Access
A novel contrast enhancement technique for diabetic retinal image pre-processing and classification
Huma Naz, Neelu Jyothi Ahuja
International Ophthalmology (2024) Vol. 45, Iss. 1
Closed Access
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