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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:

Triple-DRNet: A triple-cascade convolution neural network for diabetic retinopathy grading using fundus images
Muwei Jian, Hongyu Chen, Tao Chen, et al.
Computers in Biology and Medicine (2023) Vol. 155, pp. 106631-106631
Closed Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data
Anas Bilal, Xiaowen Liu, Muhammad Shafiq, et al.
Computers in Biology and Medicine (2024) Vol. 171, pp. 108099-108099
Closed Access | Times Cited: 47
Recent trend in medical imaging modalities and their applications in disease diagnosis: a review
Barsha Abhisheka, Saroj Kumar Biswas, Biswajit Purkayastha, et al.
Multimedia Tools and Applications (2023) Vol. 83, Iss. 14, pp. 43035-43070
Closed Access | Times Cited: 43
An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection
F. M. Javed Mehedi Shamrat, Rashiduzzaman Shakil, Sharmin, et al.
Healthcare Analytics (2024) Vol. 5, pp. 100303-100303
Open Access | Times Cited: 13
Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey
Shanshan Zhu, Changchun Xiong, Qingshan Zhong, et al.
IEEE Access (2024) Vol. 12, pp. 20540-20558
Open Access | Times Cited: 11
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 deep learning based model for diabetic retinopathy grading
Samia Akhtar, Shabib Aftab, Omar Farouk, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
An Attention-Based Swin U-Net-Based Segmentation and Hybrid Deep Learning Based Diabetic Retinopathy Classification Framework Using Fundus Images
Arti Khaparde, Shilpa S. Chapadgaonkar, Manisha Kowdiki, et al.
Sensing and Imaging (2023) Vol. 24, Iss. 1
Closed Access | Times Cited: 12
Joint ordinal regression and multiclass classification for diabetic retinopathy grading with transformers and CNNs fusion network
Lei Ma, Qihang Xu, Hanyu Hong, et al.
Applied Intelligence (2023) Vol. 53, Iss. 22, pp. 27505-27518
Closed Access | Times Cited: 11
DAU-Net: a novel U-Net with dual attention for retinal vessel segmentation
Muwei Jian, Wenjing Xu, Chuan Nie, et al.
Biomedical Physics & Engineering Express (2025) Vol. 11, Iss. 2, pp. 025009-025009
Closed Access
Deep multi-view feature fusion with data augmentation for improved diabetic retinopathy classification
Yaakoub Boualleg, Kheir Eddine Daouadi, Oussama Guehairia, et al.
Journal of Intelligent Systems (2025) Vol. 34, Iss. 1
Open Access
LECNet: Lesion semantic feature enhancement and complementary networks for diabetic retinopathy grading
Yanfei Guo, Hangli Du, Yuanke Zhang, et al.
Biomedical Signal Processing and Control (2025) Vol. 107, pp. 107813-107813
Closed Access
Adaptive Fusion Attention for enhanced classification and interpretability in medical imaging
Nagur Shareef Shaik, N. Veeranjaneulu, Jyostna Devi Bodapati
Machine Vision and Applications (2025) Vol. 36, Iss. 3
Closed Access
Hybrid deep learning framework for diabetic retinopathy classification with optimized attention AlexNet
D. S. Renu, Keiichi Saji
Computers in Biology and Medicine (2025) Vol. 190, pp. 110054-110054
Closed Access
Dynamic momentum contrastive learning network for diabetic retinopathy grading
Yanfei Guo, Chenglong Yang, Hangli Du, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 151, pp. 110604-110604
Closed Access
Deep Learning-Based Diabetic Retinopathy Segmentation System from Diagnosis to Therapeutic Decision-Making: A Real-World Multi-Center Study
Dong Li, Zhuo Deng, Heyan Li, et al.
(2025)
Closed Access
DBEF-Net: Diffusion-Based Boundary-Enhanced Fusion Network for medical image segmentation
Zhenyang Huang, Jianjun Li, Ning Mao, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124467-124467
Closed Access | Times Cited: 3
Enhancing multi-class diabetic retinopathy detection using tuned hyper-parameters and modified deep transfer learning
Yeganeh Modaresnia, Farhad Abedinzadeh Torghabeh, Seyyed Abed Hosseini
Multimedia Tools and Applications (2024) Vol. 83, Iss. 34, pp. 81455-81476
Closed Access | Times Cited: 2
complex wavelet transform with progressive network for medical imaging super resolution
Ajay Sharma, Bhavana P. Shrivastava
Multimedia Tools and Applications (2024)
Closed Access | Times Cited: 2
Two-stage deep learning classification for diabetic retinopathy using gradient weighted class activation mapping
Abderaouf M. Moustari, Youcef Brik, Bilal Attallah, et al.
Automatika (2024) Vol. 65, Iss. 3, pp. 1284-1299
Open Access | Times Cited: 2
Revolutionizing diabetic retinopathy diagnosis through advanced deep learning techniques: Harnessing the power of GAN model with transfer learning and the DiaGAN-CNN model
Mohamed R. Shoaib, Heba M. Emara, Ahmed S. Mubarak, et al.
Biomedical Signal Processing and Control (2024) Vol. 99, pp. 106790-106790
Closed Access | Times Cited: 2
Lightweight and multi-lesion segmentation model for diabetic retinopathy based on the fusion of mixed attention and ghost feature mapping
Weiwei Gao, Bo Fan, Yu Fang, et al.
Computers in Biology and Medicine (2023) Vol. 169, pp. 107854-107854
Closed Access | Times Cited: 5
Core-attributes enhanced generative adversarial networks for robust image enhancement
Shan Liu, Guoqiang Xiao, Michael S. Lew, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 131, pp. 107799-107799
Closed Access | Times Cited: 5
Advances in retinal microaneurysms detection, segmentation and datasets for the diagnosis of diabetic retinopathy: a systematic literature review
Muhammad Zeeshan Tahir, Muhammad Nasir, Sanyuan Zhang
Multimedia Tools and Applications (2024) Vol. 83, Iss. 30, pp. 74897-74935
Closed Access | Times Cited: 1
In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade
Mingkai Guo, Di Gong, Weihua Yang
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 1
Enhancing diabetic retinopathy classification using deep learning
Ghadah Alwakid, Walaa Gouda, Mamoona Humayun, et al.
Digital Health (2023) Vol. 9
Open Access | Times Cited: 2
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