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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:

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari, et al.
Computers in Biology and Medicine (2021) Vol. 136, pp. 104697-104697
Open Access | Times Cited: 140

Showing 101-125 of 140 citing articles:

A Hybrid Attention-based LSTM-XGBoost Model for Detection of ECG-based Atrial Fibrillation
Furkan BALCI
Gazi University Journal of Science Part A Engineering and Innovation (2022) Vol. 9, Iss. 3, pp. 199-210
Open Access | Times Cited: 3
The Emerging Applications of Machine Learning in the Diagnosis of Multiple Sclerosis
Anshul Sharma, Nitin Sharma, Akshita Arora, et al.
Studies in computational intelligence (2024), pp. 113-124
Closed Access
Diagnosis and Analysis of Multiple Sclerosis Disease Using Artificial Intelligence
Shafaque Aziz, Mohammad Amjad, Faiz Akram, et al.
Studies in computational intelligence (2024), pp. 125-150
Closed Access
Modeling and simulation for prediction of multiple sclerosis progression
Vishnu Prathapan, Peter Eipert, Nicole Wigger, et al.
Computers in Biology and Medicine (2024) Vol. 175, pp. 108416-108416
Open Access
A Multi-modality-based Multiple Sclerosis Detection Model
Khaled Abdrabou Abdrabou Ismail, Ashit Kumar Dutta, Abdul Rahaman Wahab Sait
Deleted Journal (2024) Vol. 3, Iss. 6
Closed Access
Ensemble Learning-based Multiple Sclerosis Detection Technique Using Magnetic Resonance Imaging
Khaled Abdrabou Abdrabou Ismail, Ashit Kumar Dutta, Abdul Rahaman Wahab Sait
Deleted Journal (2024) Vol. 3, Iss. 6
Closed Access
Enhancing trustworthiness and reliability: advance explainable artificial intelligence framework for real world Sclerosis detection
Tanzila Saba, Muhammad Mujahid, Amjad Rehman, et al.
Physica Scripta (2024) Vol. 99, Iss. 10, pp. 105017-105017
Closed Access
Deep Learning Approaches for Multiple Sclerosis Detection in MRI Images
Mohamad Marwan El Sidani, Rita Younes, Charles Yaacoub, et al.
(2024)
Closed Access
ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation—Methods and Results
Alessia Rondinella, Francesco Guarnera, Elena Crispino, et al.
Lecture notes in computer science (2024), pp. 1-16
Closed Access
Modal disentangled generative adversarial networks for bidirectional magnetic resonance image synthesis
Liming Xu, Yanrong Lei, Jie Shao, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 141, pp. 109817-109817
Closed Access
Abordagem diagnóstica de lesões cerebrais em imagens de ressonância magnética por IA
Almir Rodrigues Tavares, Thiago de Souza Franco, Cleber Silva de Oliveira, et al.
Caderno Pedagógico (2024) Vol. 21, Iss. 13, pp. e11960-e11960
Closed Access
Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques
Atefeh Rostami, Mostafa Robatjazi, Amir Dareyni, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access
MCBERT: A Multi-Modal Framework for the Diagnosis of Autism Spectrum Disorder
Kainat Khan, Rahul Katarya
Biological Psychology (2024), pp. 108976-108976
Closed Access
Early detection of multiple sclerosis through transfer learning: a weighted snapshot fusion approach with ResNet101
Ramya Palaniappan, C. Siva Ram Murthy
Multimedia Tools and Applications (2024)
Closed Access
A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks
Lijuan Bai, Jiao Wu, Li Chen, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
The Most Accurate Way of Predicting Birth Weight in China: Zhuo’s Formula
Wei Zhang, Hong Yang, Xiaoyi Guo, et al.
Current Bioinformatics (2023) Vol. 18, Iss. 3, pp. 247-254
Closed Access | Times Cited: 1
Supervised Machine Learning Models for Multiple Sclerosis Relapse Probability Prognosis using Clinical Data
Fatemeh Vakili, Zahra Vakili, Mehrdad Kargari, et al.
(2023), pp. 150-155
Closed Access | Times Cited: 1
Unsupervised Brain MRI Anomaly Detection for Multiple Sclerosis Classification
Giovanna C. Castellano, Giuseppe Placidi, Matteo Polsinelli, et al.
Lecture notes in computer science (2023), pp. 644-652
Closed Access | Times Cited: 1
Deep transfer learning for Multiple Sclerosis Detection and classification using pre-trained model
Dhyey Desai, Jayesh Gangrade, Yadvendra Singh, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
Machine Learning to Diagnose Neurodegenerative Multiple Sclerosis Disease
Jin Si Lam, Md Rakibul Hasan, Khandaker Asif Ahmed, et al.
Communications in computer and information science (2022), pp. 251-262
Closed Access | Times Cited: 2
Video supervised for 3D reconstruction from single image
Yijie Zhong, Zhengxing Sun, Shoutong Luo, et al.
Multimedia Tools and Applications (2022) Vol. 81, Iss. 11, pp. 15061-15083
Closed Access | Times Cited: 1
Pulmonary nodules recognition based on parallel cross-convolution
Yaowen Hu, Jialei Zhan, Guoxiong Zhou, et al.
Multimedia Tools and Applications (2022) Vol. 81, Iss. 20, pp. 29137-29158
Closed Access | Times Cited: 1
Your CAPTCHA Recognition Method Based on DEEP Learning Using MSER Descriptor
Deepak Kumar, Ramandeep Singh, Sukhvinder Singh Bamber
Computers, materials & continua/Computers, materials & continua (Print) (2022) Vol. 72, Iss. 2, pp. 2981-2996
Open Access | Times Cited: 1
Machine Learning Approach in Brain Imaging
Yury V. Kistenev, Denis A. Vrazhnov
(2022), pp. 203-230
Closed Access | Times Cited: 1
CNN for multiple sclerosis lesion segmentation: How many patients for a fully supervised method?
Alexandre Fenneteau, Pascal Bourdon, David Helbert, et al.
(2021), pp. 30-33
Open Access | Times Cited: 1

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