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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:

Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis
Omar Kouli, A Hassane, Dania Badran, et al.
Neuro-Oncology Advances (2022) Vol. 4, Iss. 1
Open Access | Times Cited: 17

Showing 17 citing articles:

Evolutionary Model for Brain Cancer-Grading and Classification
Faizan Ullah, Muhammad Nadeem, Mohammad Abrar, et al.
IEEE Access (2023) Vol. 11, pp. 126182-126194
Open Access | Times Cited: 39
Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives
Matia Martucci, Rosellina Russo, Francesco Schimperna, et al.
Biomedicines (2023) Vol. 11, Iss. 2, pp. 364-364
Open Access | Times Cited: 35
Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique
Saravanan Srinivasan, Prabin Selvestar Mercy Bai, Sandeep Kumar Mathivanan, et al.
Diagnostics (2023) Vol. 13, Iss. 6, pp. 1153-1153
Open Access | Times Cited: 25
Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging
Tingwei Wang, Yu-Chieh Shiao, Jia‐Sheng Hong, et al.
Mayo Clinic Proceedings Digital Health (2024) Vol. 2, Iss. 1, pp. 75-91
Open Access | Times Cited: 6
Classification of suicidality by training supervised machine learning models with brain MRI findings: A systematic review
Mohammadamin Parsaei, Fateme Taghavizanjani, Giulia Cattarinussi, et al.
Journal of Affective Disorders (2023) Vol. 340, pp. 766-791
Closed Access | Times Cited: 14
Diagnostic accuracy of cerebrospinal fluid liquid biopsy and MRI for leptomeningeal metastases in solid cancers: A systematic review and meta-analysis
Yoko Nakasu, Shoichi Deguchi, Satoshi Nakasu, et al.
Neuro-Oncology Advances (2023) Vol. 5, Iss. 1
Open Access | Times Cited: 13
Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm
Nagwan Abdel Samee, Tahir Ahmad, Noha F. Mahmoud, et al.
Healthcare (2022) Vol. 10, Iss. 12, pp. 2340-2340
Open Access | Times Cited: 18
Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews
Haishan Xu, Ting‐Ting Gong, Xin‐Jian Song, et al.
Journal of Medical Internet Research (2025) Vol. 27, pp. e53567-e53567
Open Access
Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI
P.J. Sørensen, Jonathan Frederik Carlsen, Vibeke A. Larsen, et al.
Diagnostics (2023) Vol. 13, Iss. 3, pp. 363-363
Open Access | Times Cited: 8
Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists
Qi Wan, Ji‐Soo Kim, Clifford Lindsay, et al.
Deleted Journal (2024)
Open Access | Times Cited: 1
Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia
D. Kim, Hyun Woo Oh, Chong Hyun Suh
Korean Journal of Radiology (2023) Vol. 24, Iss. 12, pp. 1179-1179
Open Access | Times Cited: 3
Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods
Guanghui Song, Xie Guan-bao, Yan Nie, et al.
Journal of Cancer Research and Clinical Oncology (2023) Vol. 149, Iss. 18, pp. 16293-16309
Closed Access | Times Cited: 2
Need for Transparency and Clinical Interpretability in Hemorrhagic Stroke Artificial Intelligence Research: Promoting Effective Clinical Application
Chae Young Lim, Beomseok Sohn, Minjung Seong, et al.
Yonsei Medical Journal (2024) Vol. 65, Iss. 10, pp. 611-611
Open Access
Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis
Tingwei Wang, Jia‐Sheng Hong, Wei‐Kai Lee, et al.
Neuroinformatics (2024) Vol. 23, Iss. 1, pp. 1-12
Open Access
Artificial Intelligence: A Tool to Help Cancer Diagnosis, Prognosis, and Treatment
Amene Saghazadeh, Nima Rezaei
Springer eBooks (2023), pp. 1-29
Closed Access
Survey on Autonomous Medical Diagnosis Tools
R Akiladevi, S Shrijeeth, Saathvik Krishnan, et al.
(2023), pp. 537-544
Closed Access
Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews (Preprint)
Haishan Xu, Ting‐Ting Gong, Xin‐Jian Song, et al.
(2023)
Closed Access
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