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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 Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI
Ejaz Ul Haq, Jianjun Huang, Huarong Xu, et al.
Computational and Mathematical Methods in Medicine (2022) Vol. 2022, pp. 1-18
Open Access | Times Cited: 52

Showing 1-25 of 52 citing articles:

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks
Saeed Iqbal, Adnan N. Qureshi, Jianqiang Li, et al.
Archives of Computational Methods in Engineering (2023) Vol. 30, Iss. 5, pp. 3173-3233
Open Access | Times Cited: 78
Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data
Gopal S. Tandel, Ashish Tiwari, O. G. Kakde, et al.
Diagnostics (2023) Vol. 13, Iss. 3, pp. 481-481
Open Access | Times Cited: 45
Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm
Muhammad Sami Ullah, Muhammad Attique Khan, Anum Masood, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 20
An Ensemble Model for the Diagnosis of Brain Tumors through MRIs
Ehsan Ghafourian, Farshad Samadifam, Heidar Fadavian, et al.
Diagnostics (2023) Vol. 13, Iss. 3, pp. 561-561
Open Access | Times Cited: 33
Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement
Matteo Ferro, Ugo Giovanni Falagario, Biagio Barone, et al.
Diagnostics (2023) Vol. 13, Iss. 13, pp. 2308-2308
Open Access | Times Cited: 28
Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system
Sadafossadat Tabatabaei, Khosro Rezaee, Min Zhu
Biomedical Signal Processing and Control (2023) Vol. 86, pp. 105119-105119
Closed Access | Times Cited: 24
AI‐Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI: Traversing the Landscape of Conventional to Explainable Approaches
Pegah Khosravi, Saber Mohammadi, Fatemeh Zahiri, et al.
Journal of Magnetic Resonance Imaging (2024)
Closed Access | Times Cited: 9
An autonomous and intelligent hybrid CNN-RNN-LSTM based approach for the detection and classification of abnormalities in brain
Priyanka Datta, Rajesh Rohilla
Multimedia Tools and Applications (2024) Vol. 83, Iss. 21, pp. 60627-60653
Closed Access | Times Cited: 5
Comparative analysis of image enhancement techniques for braintumor segmentation: contrast, histogram, and hybrid approaches
Shoffan Saifullah, Andri Pranolo, Rafał Dreżewski
E3S Web of Conferences (2024) Vol. 501, pp. 01020-01020
Open Access | Times Cited: 5
MultiModNet: An automated multimodal network for brain tumor volume determination and grading with three-dimensional u-net and deformable voxel fusion
T. Jeslin, T. Thanya
Biomedical Signal Processing and Control (2025) Vol. 103, pp. 107469-107469
Closed Access
Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping
Erfan Zarenia, A. Far, Khosro Rezaee
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Risk-based evaluation of machine learning-based classification methods used for medical devices
Martin Haimerl, Christoph Reich
BMC Medical Informatics and Decision Making (2025) Vol. 25, Iss. 1
Open Access
Optimizing brain tumor classification through feature selection and hyperparameter tuning in machine learning models
Mst. Sazia Tahosin, Md. Alif Sheakh, Taminul Islam, et al.
Informatics in Medicine Unlocked (2023) Vol. 43, pp. 101414-101414
Open Access | Times Cited: 13
Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images
Gouri Bompem, Dhanalakshmi Pandluri
International journal of intelligent engineering and systems (2024) Vol. 17, Iss. 2, pp. 39-49
Open Access | Times Cited: 4
Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis
Laís Silva Santana, Jordana Borges Camargo Diniz, Luisa Mothé Glioche Gasparri, et al.
World Neurosurgery (2024) Vol. 186, pp. 204-218.e2
Closed Access | Times Cited: 3
Review, Limitations, and future prospects of neural network approaches for brain tumor classification
Surajit Das, Rajat Subhra Goswami
Multimedia Tools and Applications (2023) Vol. 83, Iss. 15, pp. 45799-45841
Closed Access | Times Cited: 7
A mask R-CNN approach for detection and classification of brain tumours from MR images
Merve Kordemir, Kerim Kürşat Çevi̇k, Ahmet Bozkurt
Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization (2024) Vol. 11, Iss. 7
Open Access | Times Cited: 2
DEF-SwinE2NET: Dual enhanced features guided with multi-model fusion for brain tumor classification using preprocessing optimization
M. G. Abbas Malik, Adnan Saeed, Khurram Shehzad, et al.
Biomedical Signal Processing and Control (2024) Vol. 100, pp. 107079-107079
Closed Access | Times Cited: 2
A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images
Ayşe Yildirim, Murat Canayaz
Journal of Applied Biomedicine (2023) Vol. 43, Iss. 4, pp. 635-655
Closed Access | Times Cited: 6
TAGU-Net: Transformer Convolution Hybrid-Based U-Net With Attention Gate for Atypical Meningioma Segmentation
Hong Huang, Panpan Liu, Jie Liu
IEEE Access (2023) Vol. 11, pp. 53207-53223
Open Access | Times Cited: 4
Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks
Sandra Ortega‐Martorell, Iván Olier, Orlando J. Hernandez, et al.
Cancers (2023) Vol. 15, Iss. 15, pp. 4002-4002
Open Access | Times Cited: 4
Endoscopic Bladder Tissue Classification Using Seventeen Layered Deep Convolutional Neural Network
M. Shyamala Devi, J. Arun Pandian, D. Umanandhini, et al.
(2024), pp. 1-6
Closed Access | Times Cited: 1
ASA-LSTM-based brain tumor segmentation and classification in MRI images

International Journal of Advanced Technology and Engineering Exploration (2024) Vol. 11, Iss. 115
Open Access | Times Cited: 1
Brain Tumor Segmentation and Classification Using CNN Pre-Trained VGG-16 Model in MRI Images
T. Gayathri, Sundeep Kumar K.
IIUM Engineering Journal (2024) Vol. 25, Iss. 2, pp. 196-211
Open Access | Times Cited: 1
Advancements in brain tumor analysis: a comprehensive review of machine learning, hybrid deep learning, and transfer learning approaches for MRI-based classification and segmentation
Surajit Das, Rajat Subhra Goswami
Multimedia Tools and Applications (2024)
Closed Access | Times Cited: 1
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