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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:

Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network
Shu‐I Pao, Hong-Zin Lin, Ke‐Hung Chien, et al.
Journal of Ophthalmology (2020) Vol. 2020, pp. 1-7
Open Access | Times Cited: 98

Showing 26-50 of 98 citing articles:

Empirical analysis on retinal segmentation using PSO-based thresholding in diabetic retinopathy grading
Booma Devi Sekar, Subashini Parthasarathy
Biomedical Engineering / Biomedizinische Technik (2025)
Closed Access
Improved Diabetic Retinopathy Detection Using ERXG-PS Ensemble Algorithm and Modified Principal Component Analysis
K. Anitha, T. Sethukarasi, K. Abinaya, et al.
IETE Journal of Research (2025), pp. 1-13
Closed Access
A Multi-Branch Convolutional Neural Network for Screening and Staging of Diabetic Retinopathy Based on Wide-Field Optical Coherence Tomography Angiography
Bosi Dong, Xiaodong Wang, Qiang Xie, et al.
IRBM (2022) Vol. 43, Iss. 6, pp. 614-620
Closed Access | Times Cited: 18
Diabetic retinopathy detection using convolutional neural network with residual blocks
Rajasekhar Kommaraju, M.S. Anbarasi
Biomedical Signal Processing and Control (2023) Vol. 87, pp. 105494-105494
Closed Access | Times Cited: 10
GO-DBN: Gannet Optimized Deep Belief Network Based wavelet kernel ELM for Detection of Diabetic Retinopathy
S.G. Krishnamoorthy, YU Wei-feng, Jin Luo, et al.
Expert Systems with Applications (2023) Vol. 229, pp. 120408-120408
Closed Access | Times Cited: 9
Image preprocessing‐based ensemble deep learning classification of diabetic retinopathy
Peter Mácsik, Jarmila Pavlovičová, Slavomír Kajan, et al.
IET Image Processing (2023) Vol. 18, Iss. 3, pp. 807-828
Open Access | Times Cited: 9
Efficient Classification of Diabetic Retinopathy using Binary CNN
Morarjee Kolla, T. Venugopal
(2021)
Closed Access | Times Cited: 21
Enhancing Diabetic Retinopathy Detection Through Machine Learning with Restricted Boltzmann Machines
Venkateswara Rao Naramala, B. Anjanee Kumar, Vuda Sreenivasa Rao, et al.
International Journal of Advanced Computer Science and Applications (2023) Vol. 14, Iss. 9
Open Access | Times Cited: 8
Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features
Ahlam Shamsan, Ebrahim Mohammed Senan, Hamzeh Salameh Ahmad Shatnawi
PLoS ONE (2023) Vol. 18, Iss. 10, pp. e0289555-e0289555
Open Access | Times Cited: 7
Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study
Chu‐Yu Hsu, Pang‐Yen Liu, Shu-Hsin Liu, et al.
Frontiers in Cardiovascular Medicine (2022) Vol. 9
Open Access | Times Cited: 12
Detection of Diabetic Retinopathy (DR) Severity from Fundus Photographs: An Ensemble Approach Using Weighted Average
Mulagala Sandhya, Mahesh Kumar Morampudi, Rushali Grandhe, et al.
Arabian Journal for Science and Engineering (2022) Vol. 47, Iss. 8, pp. 9899-9906
Closed Access | Times Cited: 11
A broad study of machine learning and deep learning techniques for diabetic retinopathy based on feature extraction, detection and classification
Krishnan Sangeetha, K. Valarmathi, T. Kalaichelvi, et al.
Measurement Sensors (2023) Vol. 30, pp. 100951-100951
Open Access | Times Cited: 6
Classification of Diabetic Retinopathy using Pre-Trained Deep Learning Models
Inas Mudheher Raghib Kafı Al-Kamachy, Reza Hassanpour, Roya Choupani
Advances in Machine Learning & Artificial Intelligence (2024) Vol. 5, Iss. 2, pp. 01-06
Open Access | Times Cited: 2
A comprehensive review on automated systems for severity grading of diabetic retinopathy and macular edema
Mili Rosline Mathews, S. M. Anzar
International Journal of Imaging Systems and Technology (2021) Vol. 31, Iss. 4, pp. 2093-2122
Closed Access | Times Cited: 14
MVCINN: Multi-View Diabetic Retinopathy Detection Using a Deep Cross-Interaction Neural Network
Luo Xiao-ling, Chengliang Liu, Wai Keung Wong, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2023) Vol. 37, Iss. 7, pp. 8993-9001
Open Access | Times Cited: 4
Convolutional Neural Networks for Automated Diagnosis of Diabetic Retinopathy in Fundus Images
S. Rama Krishna, Naresh Cherukuri, Y Dileep Kumar, et al.
Journal of Artificial Intelligence and Technology (2023)
Open Access | Times Cited: 4
Application of AlexNet, EfficientNetV2B0, and VGG19 with Explainable AI for Cataract and Glaucoma Image Classification
Muhammad Fauzi Fayyad, Mustakim Mustakim
2022 International Electronics Symposium (IES) (2024) Vol. 53, pp. 406-412
Closed Access | Times Cited: 1
Diabetic Retinopathy Detection using Deep Learning
Koganti Nishitha Sai Sree, Dasi Veda Sree, Garikipati Hema Lakshmi, et al.
2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC) (2021), pp. 1670-1674
Closed Access | Times Cited: 10
Automatic Diagnosis of Diabetic Retinopathy using Machine Learning: A Review
Piumi Liyana Gunawardhana, Raviru Jayathilake, Yasiru Withanage, et al.
(2020)
Closed Access | Times Cited: 9
Image based early detection of diabetic retinopathy: A systematic review on Artificial Intelligence (AI) based recent trends and approaches
Anju Mishra, Laxman Singh, Mrinal Pandey, et al.
Journal of Intelligent & Fuzzy Systems (2022) Vol. 43, Iss. 5, pp. 6709-6741
Closed Access | Times Cited: 6
Detection and Classification of Diabetic Retinopathy Using Pretrained Deep Neural Networks
Abini M.A., S. Sridevi Sathya Priya
(2023) Vol. 98, pp. 1-7
Closed Access | Times Cited: 3
Improved Automatic Grading of Diabetic Retinopathy Using Deep Learning and Principal Component Analysis
Eman Mohamed, Mai Nasser Abd Elmohsen, Tamer Basha
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (2021), pp. 3898-3901
Closed Access | Times Cited: 8
Finding correlation between diabetic retinopathy and diabetes during pregnancy based on computer-aided diagnosis: a review
Debabrata Ghosh, Kuntal Chowdhury, Samya Muhuri
Multimedia Tools and Applications (2023) Vol. 83, Iss. 9, pp. 27037-27065
Closed Access | Times Cited: 3
A Robust Approach based on CNN-LSTM Network for the identification of diabetic retinopathy from Fundus Images
Sajeev Ram Arumugam, E. Anna Devi, V. Rajeshram, et al.
(2022), pp. 152-156
Closed Access | Times Cited: 5
Diabetic Retinopathy Detection and Grading using Deep learning
Mohamed BERBAR
Menoufia Journal of Electronic Engineering Research (2022), pp. 11-20
Open Access | Times Cited: 5

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