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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:

Glioma Classification Using Deep Radiomics
Subhashis Banerjee, Sushmita Mitra, Francesco Masulli, et al.
SN Computer Science (2020) Vol. 1, Iss. 4
Open Access | Times Cited: 24

Showing 24 citing articles:

Deep Learning for Screening COVID-19 using Chest X-Ray Images
Sanhita Basu, Sushmita Mitra, Nilanjan Saha
2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2020), pp. 2521-2527
Open Access | Times Cited: 165
Brain Tumor Segmentation from 3D MRI Scans Using U-Net
Sidratul Montaha, Sami Azam, A. K. M. Rakibul Haque Rafid, et al.
SN Computer Science (2023) Vol. 4, Iss. 4
Open Access | Times Cited: 24
Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology
Carla Pitarch, Gülnur Ungan, Margarida Julià‐Sapé, et al.
Cancers (2024) Vol. 16, Iss. 2, pp. 300-300
Open Access | Times Cited: 5
Classification of Brain Tumors in MRI Images Using Deep Learning
Rahul Jadhav, G. Sudhagar
Communications in computer and information science (2025), pp. 45-56
Closed Access
Observing deep radiomics for the classification of glioma grades
Kazuma Kobayashi, Mototaka Miyake, Masamichi Takahashi, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 38
Performance enhancement of MRI-based brain tumor classification using suitable segmentation method and deep learning-based ensemble algorithm
Gopal S. Tandel, Ashish Tiwari, O. G. Kakde
Biomedical Signal Processing and Control (2022) Vol. 78, pp. 104018-104018
Closed Access | Times Cited: 25
Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1-weighted imaging: a robust, multi-institutional study
Jialin Ding, Zhao Ru-bin, Qingtao Qiu, et al.
Quantitative Imaging in Medicine and Surgery (2021) Vol. 12, Iss. 2, pp. 1517-1528
Open Access | Times Cited: 25
An automated approach for predicting glioma grade and survival of LGG patients using CNN and radiomics
Chenan Xu, Yuanyuan Peng, Weifang Zhu, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 18
Combining Radiology and Pathology for Automatic Glioma Classification
Xiyue Wang, Ruijie Wang, Sen Yang, et al.
Frontiers in Bioengineering and Biotechnology (2022) Vol. 10
Open Access | Times Cited: 17
A Novel System for Precise Grading of Glioma
Ahmed Alksas, Mohamed Shehata, Hala Atef, et al.
Bioengineering (2022) Vol. 9, Iss. 10, pp. 532-532
Open Access | Times Cited: 12
Predictive Patient-Centric Healthcare
Ajay B. Gadicha, Vijay B. Gadicha, Mohammad Zuhair
Advances in electronic government, digital divide, and regional development book series (2024), pp. 139-152
Closed Access | Times Cited: 1
Towards a computer aided diagnosis (CAD) for brain MRI glioblastomas tumor exploration based on a deep convolutional neuronal networks (D-CNN) architectures
Hiba Mzoughi, Ines Njeh, Mohamed Ben Slima, et al.
Multimedia Tools and Applications (2020) Vol. 80, Iss. 1, pp. 899-919
Open Access | Times Cited: 13
Recent Trends on Brain Tumor Detection Using Hybrid Deep Learning Methods
Anisa C. Buchade, MVV Prasad Kantipudi
Revue d intelligence artificielle (2024) Vol. 38, Iss. 1, pp. 103-113
Open Access | Times Cited: 1
Deep Learning With Radiogenomics Towards Personalized Management of Gliomas
Sushmita Mitra
IEEE Reviews in Biomedical Engineering (2021) Vol. 16, pp. 579-593
Closed Access | Times Cited: 9
Deep cross-view co-regularized representation learning for glioma subtype identification
Zhenyuan Ning, Chao Tu, Xiaohui Di, et al.
Medical Image Analysis (2021) Vol. 73, pp. 102160-102160
Closed Access | Times Cited: 9
A hierarchical machine learning model based on Glioblastoma patients' clinical, biomedical, and image data to analyze their treatment plans
Mohammad Mahdi Ershadi, Zeinab Rahimi Rise, Seyed Taghi Akhavan Niaki
Computers in Biology and Medicine (2022) Vol. 150, pp. 106159-106159
Closed Access | Times Cited: 5
Proposed Approaches for Brain Tumors Detection Techniques Using Convolutional Neural Networks
Somaya Feshawy, Waleed Saad, Mona Shokair, et al.
International Journal of Telecommunications (2022) Vol. 02, Iss. 01, pp. 1-14
Open Access | Times Cited: 2
Observing Deep Radiomics for the Classification of Glioma Grades
Kazuma Kobayashi, Mototaka Miyake, Masamichi Takahashi, et al.
Research Square (Research Square) (2021)
Open Access | Times Cited: 2
Automatic 1p/19q co-deletion identification of gliomas by MRI using deep learning U-net network
Kai Zhao, Boyuan Li, Kai Zhang, et al.
Computers & Electrical Engineering (2022) Vol. 105, pp. 108482-108482
Closed Access | Times Cited: 1
A Comprehensive Non-invasive System for Early Grading of Gliomas
Ahmed Alksas, Mohamed Shehata, Hala Atef, et al.
2022 26th International Conference on Pattern Recognition (ICPR) (2022), pp. 4371-4377
Closed Access | Times Cited: 1
Automatic Glioma Grading Based on Two-Stage Networks by Integrating Pathology and MRI Images
Xiyue Wang, Sen Yang, Xiyi Wu
Lecture notes in computer science (2021), pp. 455-464
Closed Access | Times Cited: 1
Open Data to Support CANCER Science—A Bioinformatics Perspective on Glioma Research
Fleur Jeanquartier, Claire Jean-Quartier, Sarah Stryeck, et al.
Onco (2021) Vol. 1, Iss. 2, pp. 219-229
Open Access | Times Cited: 1

V. Kakulapati
International Journal of Pharmaceutical Sciences and Research (2023) Vol. 14, Iss. 4
Open Access
Glioma grade prediction using a cross-fusion network based on unsegmented multi-sequence magnetic resonance images
Qijian Chen, Lihui Wang, Shunchao Guo, et al.
2022 16th IEEE International Conference on Signal Processing (ICSP) (2022), pp. 447-451
Closed Access
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