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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:

Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning
Natasha Shaukat, Javeria Amin, Muhammad Sharif, et al.
Journal of Personalized Medicine (2022) Vol. 12, Iss. 9, pp. 1454-1454
Open Access | Times Cited: 21

Showing 21 citing articles:

Detection and classification of red lesions from retinal images for diabetic retinopathy detection using deep learning models
P. Saranya, R Pranati, Sneha Shruti Patro
Multimedia Tools and Applications (2023) Vol. 82, Iss. 25, pp. 39327-39347
Closed Access | Times Cited: 43
EdgeSVDNet: 5G-Enabled Detection and Classification of Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images
Anas Bilal, Xiaowen Liu, Talha Imtiaz Baig, et al.
Electronics (2023) Vol. 12, Iss. 19, pp. 4094-4094
Open Access | Times Cited: 34
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
Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
Natasha Shaukat, Javeria Amin, Muhammad Imran Sharif, et al.
Applied Sciences (2023) Vol. 13, Iss. 5, pp. 3108-3108
Open Access | Times Cited: 21
Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection
Nabeela Yaqoob, Muhammad Attique Khan, Saleha Masood, et al.
Frontiers in Computational Neuroscience (2024) Vol. 18
Open Access | Times Cited: 5
Advancements in Diabetic Retinopathy Detection Using Deep Learning
Sonia Sarmah, Manasi Hazarika, Pranab K. Das, et al.
Studies in computational intelligence (2025), pp. 91-117
Closed Access
Classification of diabetic retinopathy grades using CNN feature extraction to segment the lesion
M. Swathi, S. Venkata Lakshmi
International Journal of Computational and Experimental Science and Engineering (2024) Vol. 10, Iss. 4
Open Access | Times Cited: 4
A Deep Learning Grading Classification of Diabetic Retinopathy on Retinal Fundus Images with Bio-inspired Optimization
R. Ramesh, Selvarajan Sathiamoorthy
Engineering Technology & Applied Science Research (2023) Vol. 13, Iss. 4, pp. 11248-11252
Open Access | Times Cited: 11
A Comprehensive Review of Diabetic Retinopathy Detection and Grading Based on Deep Learning and Metaheuristic Optimization Techniques
A. Mary Dayana, W. R. Sam Emmanuel
Archives of Computational Methods in Engineering (2023) Vol. 30, Iss. 7, pp. 4565-4599
Closed Access | Times Cited: 9
Explainable Neural Network for Classification of Cotton Leaf Diseases
Javeria Amin, Muhammad Almas Anjum, Muhammad Sharif, et al.
Agriculture (2022) Vol. 12, Iss. 12, pp. 2029-2029
Open Access | Times Cited: 11
Segmentation of diabetic retinopathy images using deep feature fused residual with U-Net
Meshal Alharbi, Deepak Gupta
Alexandria Engineering Journal (2023) Vol. 83, pp. 307-325
Open Access | Times Cited: 6
An eccentric Iter Net–based Improved Intelligent Water Drop (I2WD) feature selection and Discriminated Multi-Instance Classification (DMIC) models for diabetic retinopathy detection
Vinoth Rathinam, Sasireka Rajendran, K. Valarmathi
International Journal of Diabetes in Developing Countries (2024)
Closed Access | Times Cited: 1
Automated grading of diabetic retinopathy and radiomics analysis on ultra-wide optical coherence tomography angiography scans
Vivek Noel Soren, H.S. Prajwal, Vaanathi Sundaresan
Image and Vision Computing (2024), pp. 105292-105292
Closed Access | Times Cited: 1
Joint DR-DME grading classification using optimal feature selection-based deep graph correlation network
Purna Chandra Reddy, Kiran Kumar Gurrala
Applied Soft Computing (2023) Vol. 149, pp. 110981-110981
Closed Access | Times Cited: 3
Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN
Heba M. El‐Hoseny, Heba F. Elsepae, Wael A. Mohamed, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2023) Vol. 77, Iss. 2, pp. 1855-1872
Open Access | Times Cited: 2
Enhanced Feature Representation of Retinal Fundus Images using Multi-Channel Fusion
Aritro Santra, Jethe Krushi, Anu Shaju Areeckal
(2024) Vol. 18, pp. 652-656
Closed Access
Detection of Diabetic Retinopathy Using Discrete Wavelet-Based Center-Symmetric Local Binary Pattern and Statistical Features
Imtiyaz Ahmad, Vibhav Prakash Singh, Manoj Madhava Gore
Deleted Journal (2024)
Closed Access
DRSegNet: A cutting-edge approach to Diabetic Retinopathy segmentation and classification using parameter-aware Nature-Inspired optimization
Sundreen Asad Kamal, Youtian Du, Majdi Khalid, et al.
PLoS ONE (2024) Vol. 19, Iss. 12, pp. e0312016-e0312016
Open Access
An Improved Residual U-Net for Segmentation of Multiple Structures in Fundus Images
S. Balaji, B. Karthik
Research Square (Research Square) (2023)
Open Access
DRGNet: Diabetic Retinopathy Grading Network Using Data Balancing Integrated Transfer Learning with Graph-based KNN Classification
Swetha Pesaru, M. Naresh Kumar, Vishnu Vardhan Bulusu
International journal of intelligent engineering and systems (2023) Vol. 16, Iss. 6, pp. 411-421
Open Access
Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification
Ashok Bhansali, Raj Kumar Patra, Mohamed Abouhawwash, et al.
Frontiers in Bioengineering and Biotechnology (2023) Vol. 11
Open Access
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