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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:

A new deep learning approach for the retinal hard exudates detection based on superpixel multi-feature extraction and patch-based CNN
Chenxi Huang, Yongshuo Zong, Yimin Ding, et al.
Neurocomputing (2020) Vol. 452, pp. 521-533
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images
G Balla, Mohammad Farukh Hashmi, Zong Woo Geem, et al.
IEEE Access (2022) Vol. 10, pp. 57796-57823
Open Access | Times Cited: 55
Retinal blood vessel segmentation using pixel-based feature vector
Buket Toptaş, Davut Hanbay
Biomedical Signal Processing and Control (2021) Vol. 70, pp. 103053-103053
Closed Access | Times Cited: 44
A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
Anila Sebastian, Omar Elharrouss, Somaya Al‐Maadeed, et al.
Applied Sciences (2023) Vol. 13, Iss. 8, pp. 5111-5111
Open Access | Times Cited: 17
nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
Shubin Wang, Yuanyuan Chen, Yi Zhang
Applied Sciences (2024) Vol. 14, Iss. 1, pp. 411-411
Open Access | Times Cited: 6
A novel joint learning framework combining fuzzy C-multiple-means clustering and spectral clustering for superpixel-based image segmentation
Chengmao Wu, Pengfei Gai
Digital Signal Processing (2025) Vol. 161, pp. 105083-105083
Closed Access
A Novel Hybrid Optic Disc Detection and Fovea Localization Method Integrating Region-Based Convnet and Mathematical Approach
Barış Dinç, Yasin Kaya
Wireless Personal Communications (2023) Vol. 129, Iss. 4, pp. 2727-2748
Closed Access | Times Cited: 11
Machine Learning Styles for Diabetic Retinopathy Detection: A Review and Bibliometric Analysis
Shyamala Subramanian, Sashikala Mishra, Shruti Patil, et al.
Big Data and Cognitive Computing (2022) Vol. 6, Iss. 4, pp. 154-154
Open Access | Times Cited: 13
A novel retinal image segmentation using rSVM boosted convolutional neural network for exudates detection
Swarup Kr Ghosh, Anupam Ghosh
Biomedical Signal Processing and Control (2021) Vol. 68, pp. 102785-102785
Closed Access | Times Cited: 16
A Multi-Layer stacked ensemble classifier model for improved classification accuracy of Maculopathy gradation
Moumita Sahoo, Santanu Ghorai, Saurabh Pal, et al.
Displays (2022) Vol. 73, pp. 102213-102213
Closed Access | Times Cited: 11
SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss
Geng-Xin Xu, Chuan-Xian Ren
Neurocomputing (2022) Vol. 523, pp. 199-212
Open Access | Times Cited: 11
Retinal multi-lesion segmentation by reinforcing single-lesion guidance with multi-view learning
Liyun Zhang, Zhiwen Fang, Ting Li, et al.
Biomedical Signal Processing and Control (2023) Vol. 86, pp. 105349-105349
Closed Access | Times Cited: 4
Comparison of quantification of intraretinal hard exudates between optical coherence tomography en face image versus fundus photography
Donghee Park, Kyoung Lae Kim, Sung Pyo Park, et al.
Indian Journal of Ophthalmology (2024) Vol. 72, Iss. Suppl 2, pp. S280-S296
Open Access | Times Cited: 1
Enhancing retinal fundus image classification through Active Gradient Deep Convolutional Neural Network and Red Spider Optimization
S. Krishnakumar, Archana Naganathan
Neural Computing and Applications (2024) Vol. 36, Iss. 26, pp. 16607-16619
Closed Access | Times Cited: 1
MAS-Net: Multi-Attention Hybrid Network for Superpixel Segmentation
Guanghui Yan, Chenzhen Wei, Xiaohong Jia, et al.
Symmetry (2024) Vol. 16, Iss. 8, pp. 1000-1000
Open Access | Times Cited: 1
Efficient diabetic retinopathy diagnosis through U-Net – KNN integration in retinal fundus images
V. Selvakumar, C. Akila
Automatika (2023) Vol. 64, Iss. 4, pp. 1148-1157
Open Access | Times Cited: 3
Segmentation of Hard Exudates in Retina Fundus Images Using BCDU-Net
Nafise Ameri, Nasser Shoeibi, Mojtaba Abrishami
(2022)
Closed Access | Times Cited: 5
A Novel Soft Clustering Method for Detection of Exudates
Kittipol Wisaeng
Computer Systems Science and Engineering (2023) Vol. 46, Iss. 1, pp. 1039-1058
Open Access | Times Cited: 2
Hard Exudates Segmentation From Retinal Fundus Images
Sneh Shah, S.M.A. Shah, Vaishnav Kamarti, et al.
2022 IEEE 6th Conference on Information and Communication Technology (CICT) (2023), pp. 1-6
Closed Access | Times Cited: 2
Computer aided diagnosis systems using deep learning for retinal diseases: A survey
G. Sivapriya, P. Keerthika
Materials Today Proceedings (2022) Vol. 58, pp. 286-292
Closed Access | Times Cited: 4
Hard Exudates Segmentation in Diabetic retinopathy using DiaRetDB1
Ma Yinghua, Yang Heng, R. Amarnath, et al.
IEEE Access (2024) Vol. 12, pp. 126486-126502
Open Access
Comparative Analysis of Superpixel and Gabor Methods for Exudate Feature Extraction in Diabetic Retinopathy Fundus Images
Nur Munirah Suhaimi, Rosdiyana Samad, Nor Rul Hasma Abdullah, et al.
Lecture notes in electrical engineering (2024), pp. 123-136
Closed Access
DenseExudatesNet: a novel approach for hard exudates detection in retinal images using deep learning
C. Pratheeba, Nivedita Rufus
International Journal of Machine Learning and Cybernetics (2024)
Closed Access
Detection and Segmentation of Retinopathy Diseases using EAD-Net with Fundus Images
G. Sivapriya, V. Praveen, S. Saranya, et al.
2022 International Conference on Computer Communication and Informatics (ICCCI) (2023)
Closed Access | Times Cited: 1
Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN
Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Pitipol Choopong, et al.
Applied Computing and Informatics (2022)
Open Access | Times Cited: 1
Modified U-Net Architecture for Diabetic Retinopathy Fundus Image Segmentation
Shubin Wang, Yuanyuan Chen, Yi Zhang
(2023), pp. 527-532
Closed Access
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