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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 deep learning approach for brain tumor classification using MRI images
Muhammad Aamir, Zia-ur Rahman, Zaheer Ahmed Dayo, et al.
Computers & Electrical Engineering (2022) Vol. 101, pp. 108105-108105
Open Access | Times Cited: 143

Showing 26-50 of 143 citing articles:

Enhancing Medical Diagnostics: Integrating AI for precise Brain Tumour Detection
Arohee Sinha, Tarun Kumar
Procedia Computer Science (2024) Vol. 235, pp. 456-467
Open Access | Times Cited: 5
Brain tumor classification from MRI images using exponential-Walruses hunting optimization driven SqueezeNet
Pendela Kanchanamala, Ramesh Karnati, Ravi Kumar Tammineni
Expert Systems with Applications (2025) Vol. 271, pp. 126633-126633
Closed Access
Advancing brain tumor segmentation and grading through integration of FusionNet and IBCO-based ALCResNet
Abbas Rehman, Naijie Gu, Asma Aldrees, et al.
Image and Vision Computing (2025), pp. 105432-105432
Closed Access
Exploring brain tumor detection through artificial intelligence
Ashish Kumar, Shikha Bhalla
Elsevier eBooks (2025), pp. 93-120
Closed Access
VSA-GCNN: Attention Guided Graph Neural Networks for Brain Tumor Segmentation and Classification
Kambham Pratap Joshi, Vishruth B. Gowda, B D Parameshachari, et al.
Big Data and Cognitive Computing (2025) Vol. 9, Iss. 2, pp. 29-29
Open Access
Subclass Classification of Gliomas Using MRI Fusion Technique
Kiranmayee Janardhan, T. Christy Bobby
SN Computer Science (2025) Vol. 6, Iss. 2
Closed Access
A Comparative Analysis Between CNNs and ViTs for MRI-based Brain Tumor Classification
Yuteng Zhang
Highlights in Science Engineering and Technology (2025) Vol. 124, pp. 30-37
Closed Access
Lightweight-CancerNet: a deep learning approach for brain tumor detection
Asif Raza, Muhammad Iqbal
PeerJ Computer Science (2025) Vol. 11, pp. e2670-e2670
Open Access
Advancing Cancer Diagnosis: Federated Learning for Tumor Classification
Siddharth Kanakrai Upadhyay, Khushi Singh, G Suganeshwari
Smart innovation, systems and technologies (2025), pp. 1-13
Closed Access
Detection of MRI brain tumor using residual skip block based modified MobileNet model
Saif Ur Rehman Khan, Ming Zhao, Yangfan Li
Cluster Computing (2025) Vol. 28, Iss. 4
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
Deep Learning for Early Brain Tumor Detection
Ruchira Goel, Updesh Kumar Jaiswal, Pancham Singh, et al.
IGI Global eBooks (2025), pp. 145-180
Closed Access
DeepLabV3, IBCO-based ALCResNet: A fully automated classification, and grading system for brain tumor
Muhammad Yaqub, Jinchao Feng, Shahzad Ahmed, et al.
Alexandria Engineering Journal (2023) Vol. 76, pp. 609-627
Open Access | Times Cited: 15
YoDenBi-NET: YOLO + DenseNet + Bi-LSTM-based hybrid deep learning model for brain tumor classification
Abdülkadir Karacı, Kemal Akyol
Neural Computing and Applications (2023) Vol. 35, Iss. 17, pp. 12583-12598
Closed Access | Times Cited: 14
Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches
Chetan Swarup, Kamred Udham Singh, Ankit Kumar, et al.
Electronic Research Archive (2023) Vol. 31, Iss. 5, pp. 2900-2924
Open Access | Times Cited: 14
Refined Automatic Brain Tumor Classification Using Hybrid Convolutional Neural Networks for MRI Scans
F. E. Al‐Tahhan, Ghada A. Khouqeer, Sarmad Saadi, et al.
Diagnostics (2023) Vol. 13, Iss. 5, pp. 864-864
Open Access | Times Cited: 12
Deep transfer learning model for brain tumor segmentation and classification using UNet and chopped VGGNet
Jayashree Shedbalkar, Kappargaon S. Prabhushetty
Indonesian Journal of Electrical Engineering and Computer Science (2024) Vol. 33, Iss. 3, pp. 1405-1405
Open Access | Times Cited: 4
Vision transformers (ViT) and deep convolutional neural network (D-CNN)-based models for MRI brain primary tumors images multi-classification supported by explainable artificial intelligence (XAI)
Hiba Mzoughi, Ines Njeh, Mohamed Benslima, et al.
The Visual Computer (2024)
Closed Access | Times Cited: 4
WFT-Fati-Dec: Enhanced Fatigue Detection AI System Based on Wavelet Denoising and Fourier Transform
Ahmed Sedik, Mohamed Marey, Hala Mostafa
Applied Sciences (2023) Vol. 13, Iss. 5, pp. 2785-2785
Open Access | Times Cited: 11
A novel Gateaux derivatives with efficient DCNN-Resunet method for segmenting multi-class brain tumor
Anita Murmu, Piyush Kumar
Medical & Biological Engineering & Computing (2023) Vol. 61, Iss. 8, pp. 2115-2138
Closed Access | Times Cited: 11
SSBTCNet: Semi-Supervised Brain Tumor Classification Network
Zubair Atha, Jyotismita Chaki
IEEE Access (2023) Vol. 11, pp. 141485-141499
Open Access | Times Cited: 10
$$\mathcal {B}\text {rain}{\mathcal{M}\mathcal{N}}\text {et}$$: a unified neural network architecture for brain image classification
Sudip Ghosh, Deepti, Shivam Gupta
Network Modeling Analysis in Health Informatics and Bioinformatics (2024) Vol. 13, Iss. 1
Closed Access | Times Cited: 3
An Effective analysis of brain tumor detection using deep learning
Yenumala Sankararao, Syed Khasim
EAI Endorsed Transactions on Pervasive Health and Technology (2024) Vol. 10
Open Access | Times Cited: 3
Computer-Aided Diagnostic System for Brain Tumor Classification using Explainable AI
S. Padmapriya, M.S. Gayathri Devi
(2024), pp. 1-6
Closed Access | Times Cited: 3
Feature extraction of multimodal medical image fusion using novel deep learning and contrast enhancement method
Jameel Ahmed Bhutto, Guosong Jiang, Ziaur Rahman, et al.
Applied Intelligence (2024) Vol. 54, Iss. 7, pp. 5907-5930
Closed Access | Times Cited: 3

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