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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:

Using a Noisy U-Net for Detecting Lung Nodule Candidates
Wenkai Huang, Lingkai Hu
IEEE Access (2019) Vol. 7, pp. 67905-67915
Open Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

Deep Learning Techniques to Diagnose Lung Cancer
Lulu Wang
Cancers (2022) Vol. 14, Iss. 22, pp. 5569-5569
Open Access | Times Cited: 107
Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithms
Laxmikant Tiwari, Rohit Raja, Vineet Kumar Awasthi, et al.
Measurement (2020) Vol. 172, pp. 108882-108882
Closed Access | Times Cited: 64
A Hybrid Deep Learning Model Combining AgresNet, YOLO, and CNN for Lung Tumor Segmentation and Classification
Princy Magdaline P., Ganesh Babu T. R., R. Praveena, et al.
Journal of Innovative Image Processing (2025) Vol. 6, Iss. 4, pp. 472-498
Open Access
AttentNet: Fully Convolutional 3D Attention for Lung Nodule Detection
Majedaldein Almahasneh, Xianghua Xie, Adeline Paiement
SN Computer Science (2025) Vol. 6, Iss. 3
Open Access
An amalgamation of vision transformer with convolutional neural network for automatic lung tumor segmentation
Shweta Tyagi, Devidas T. Kushnure, Sanjay N. Talbar
Computerized Medical Imaging and Graphics (2023) Vol. 108, pp. 102258-102258
Closed Access | Times Cited: 13
Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets
Kazuhiro Suzuki, Yujiro Otsuka, Yukihiro Nomura, et al.
Academic Radiology (2020) Vol. 29, pp. S11-S17
Open Access | Times Cited: 32
Optimized Lung Nodule Prediction Model for Lung Cancer Using Contour Features Extraction
Faiyaz Ahmad, U. Hariharan, S. Karthick, et al.
Optical Memory and Neural Networks (2023) Vol. 32, Iss. 2, pp. 126-136
Closed Access | Times Cited: 11
A deep learning system that generates quantitative CT reports for diagnosing pulmonary Tuberculosis
Xukun Li, Yukun Zhou, Peng Du, et al.
Applied Intelligence (2020) Vol. 51, Iss. 6, pp. 4082-4093
Open Access | Times Cited: 26
Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models
Dat Tien Nguyen, Min Beom Lee, Tuyen Danh Pham, et al.
Sensors (2020) Vol. 20, Iss. 21, pp. 5982-5982
Open Access | Times Cited: 25
Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation
Eid Albalawi, Eali Stephen Neal Joshua, N. Marline Joys Kumari, et al.
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 2
Multiscale 3D TransUNet-aided Tumor Segmentation and Multi-Cascaded Model for Lung Cancer Diagnosis System from 3D CT Images with Fused Feature Pool Formation
GILBERT langat, Beiji Zou, Xiaoyan Kui, et al.
International Journal for Multiscale Computational Engineering (2024) Vol. 22, Iss. 6, pp. 31-64
Closed Access | Times Cited: 1
Robust and accurate pulmonary nodule detection with self-supervised feature learning on domain adaptation
Jingya Liu, Liangliang Cao, Oğuz Akın, et al.
Frontiers in Radiology (2022) Vol. 2
Open Access | Times Cited: 7
GD-StarGAN: Multi-domain image-to-image translation in garment design
Yangyun Shen, Runnan Huang, Wenkai Huang
PLoS ONE (2020) Vol. 15, Iss. 4, pp. e0231719-e0231719
Open Access | Times Cited: 7
Survey on Lung Cancer Detection Techniques
Varsha Prakash, Prasanth Vas
2021 International Conference on Computational Performance Evaluation (ComPE) (2020), pp. 800-803
Closed Access | Times Cited: 4
Semantic Segmentation of Lungs using U-Net
R. Praveena, T. R. Ganesh Babu, S. Suvetha, et al.
Journal of Innovative Image Processing (2024) Vol. 6, Iss. 2, pp. 154-163
Open Access
Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography
Mary K. Montgomery, Chong Duan, Lisa K. Manzuk, et al.
Translational Oncology (2023) Vol. 40, pp. 101833-101833
Open Access | Times Cited: 1
Stage Identification and Classification of Lung Cancer using Deep Convolutional Neural Network
Varsha Prakash, Smitha Vas.P
International Journal of Advanced Computer Science and Applications (2020) Vol. 11, Iss. 7
Open Access | Times Cited: 3
Lung carcinoma detection at premature stage using deep learning techniques
Isha Bhatia, Aarti Aarti
AIP conference proceedings (2022) Vol. 2635, pp. 050009-050009
Closed Access | Times Cited: 2
Researches Advanced in Automatic Lung Cancer Diagnosis based on Convolutional Neural Networks
Fengyun Chen, Huizi Qian, Xingyu Zhu
2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI) (2022), pp. 882-888
Closed Access | Times Cited: 1
A Novel Approach of Lung Tumor Segmentation Using a 3D Deep Convolutional Neural Network
Shweta Tyagi, Sanjay N. Talbar, Abhishek Mahajan
Advances in healthcare information systems and administration book series (2021), pp. 1-16
Closed Access | Times Cited: 1
Deep Learning Methods for Lung Cancer Nodule Classification: A Survey
Pavan Kumar Illa, T. Senthil Kumar, F. Syed Anwar Hussainy
Journal of Mobile Multimedia (2021)
Open Access | Times Cited: 1
Multiscale 3d Transunet-Aided Tumor Segmentation and Multi-Cascaded Model for Lung Cancer Diagnosis System from 3d Ct Images with Fused Feature Pool Formation
GILBERT langat, Beiji Zou, Xiaoyan Kui, et al.
(2023)
Closed Access
Application of Deep Learning Algorithms in Medical Image Processing: A Survey
B. Santhi, A.M. Swetha, A.M. Ashutosh
(2022), pp. 341-378
Closed Access
Deep Learning-Based Cancerous Lung Nodule Detection in Computed Tomography Imageries
Sangaraju V. Kumar, Fei Chen, Sumi Kim, et al.
Lecture notes in networks and systems (2022), pp. 44-52
Closed Access
A Study of Methods Researched for Lung Cancer Detection
Aishwarya Nalawade, Shrinivas A Patil
Soft Computing Research Society eBooks (2022), pp. 493-499
Open Access
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