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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 Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model
Carlos Santos, Marílton Sanchotene de Aguiar, Daniel Welfer, et al.
Sensors (2022) Vol. 22, Iss. 17, pp. 6441-6441
Open Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)
Mohammed Gamal Ragab, Said Jadid Abdulkadir, Amgad Muneer, et al.
IEEE Access (2024) Vol. 12, pp. 57815-57836
Open Access | Times Cited: 34
Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m
Yong-Suk Lee, Maheshkumar Prakash Patil, Jeong Gyu Kim, et al.
Plants (2025) Vol. 14, Iss. 5, pp. 653-653
Open Access | Times Cited: 2
Enhanced object detection in pediatric bronchoscopy images using YOLO-based algorithms with CBAM attention mechanism
Jianqi Yan, Yifan Zeng, Junhong Lin, et al.
Heliyon (2024) Vol. 10, Iss. 12, pp. e32678-e32678
Open Access | Times Cited: 8
An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5
Guijuan Lin, Keyu Liu, Xuke Xia, et al.
Sensors (2022) Vol. 23, Iss. 1, pp. 97-97
Open Access | Times Cited: 34
A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)
Rizwan Qureshi, Mohammed Gamal Ragab, SAID JADID ABDULKADER, et al.
(2023)
Open Access | Times Cited: 20
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
AIE-YOLO: Auxiliary Information Enhanced YOLO for Small Object Detection
Bingnan Yan, Jiaxin Li, Zhaozhao Yang, et al.
Sensors (2022) Vol. 22, Iss. 21, pp. 8221-8221
Open Access | Times Cited: 24
Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9
Nicola Rizzieri, Luca Dall’Asta, Māris Ozoliņš
Vision (2024) Vol. 8, Iss. 3, pp. 48-48
Open Access | Times Cited: 4
Models to Identify Small Brain White Matter Hyperintensity Lesions
Darwin Castillo, María José Rodríguez-Álvarez, René Samaniego, et al.
Applied Sciences (2025) Vol. 15, Iss. 5, pp. 2830-2830
Open Access
A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)
Rizwan Qureshi, Mohammed Gamal Ragab, Said Jadid Abdulkader, et al.
(2023)
Open Access | Times Cited: 11
Dual-Branch U-Net Architecture for Retinal Lesions Segmentation on Fundus Image
Ming Yin, Toufique Ahmed Soomro, Fayyaz Ali Jandan, et al.
IEEE Access (2023) Vol. 11, pp. 130451-130465
Open Access | Times Cited: 7
A New Approach for Fundus Lesions Instance Segmentation Based on Mask R-CNN X101-FPN Pre-Trained Architecture
Carlos Santos, Marílton Sanchotene de Aguiar, Daniel Welfer, et al.
IEEE Access (2023) Vol. 11, pp. 43603-43618
Open Access | Times Cited: 6
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
DRAT: A semi-supervised tool for automatic annotation of lesions caused by diabetic retinopathy
Marcelo Dias, Carlos Santos, Marílton Sanchotene de Aguiar, et al.
(2024), pp. 1-9
Closed Access | Times Cited: 1
Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method
Vidas Raudonis, A. Kairys, Rasa Verkauskienė, et al.
Sensors (2023) Vol. 23, Iss. 7, pp. 3431-3431
Open Access | Times Cited: 4
U-Net based Segmentation and Transfer Learning Based-Classification for Diabetic-Retinopathy Diagnosis
B D Parameshachari, B M Nalini, H M LeenaShruthi, et al.
(2023), pp. 01-07
Closed Access | Times Cited: 4
Neighbored-attention U-net (NAU-net) for diabetic retinopathy image segmentation
Tingting Zhao, Yawen Guan, Dan Tu, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 3
A Deep Learning Approach to Hard Exudates Detection and Disorganization of Retinal Inner Layers Identification on OCT images
Lisa Toto, Anna Maria Romano, Marco Pavan, et al.
Research Square (Research Square) (2024)
Open Access
Enhancing Bus Number Detection Efficiency with Transfer Learning and Fine-Tuned Optical Character Recognition
Rio Arifando, Shinji Eto, Chikamune Wada
(2024), pp. 106-111
Closed Access
Deep Learning Technique to detect Brain tumor disease using YOLO v8
G. Vineela, G. Harsha Vardhan, Ch. Kesava Rao, et al.
(2024), pp. 1-5
Closed Access
A Hybrid ArtInt Model to Predict Diabetic Retinopathy
Prakash Arumugam, Divya Bhavani Mohan
Advances in healthcare information systems and administration book series (2024), pp. 86-105
Closed Access
Deep learning based MA detection with modified ResNet-50
Bindhya PS, R. Chitra, Bibin Raj VS
Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization (2024) Vol. 12, Iss. 1
Open Access
A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images
Lisa Toto, Anna Maria Romano, Marco Pavan, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
NetraDeep: An Integrated Deep Learning and Image Processing System for Precise Detection of Hard Exudates
V.K. Agrawal, Vijay Kumar, S.C. Sharma, et al.
ACM Transactions on Computing for Healthcare (2024) Vol. 5, Iss. 4, pp. 1-34
Closed Access
Harnessing YOLOv9 for Enhanced Detection of Lung Cancer: A Deep Learning Approach
Norhan Elnady, Aya Adel, W. Badawy
(2024) Vol. 129, pp. 518-523
Closed Access
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