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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:

Depression detection from sMRI and rs-fMRI images using machine learning
Marzieh Mousavian, Jianhua Chen, Zachary Traylor, et al.
Journal of Intelligent Information Systems (2021) Vol. 57, Iss. 2, pp. 395-418
Closed Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

A comprehensive review of predictive analytics models for mental illness using machine learning algorithms
Md. Monirul Islam, Shahriar Hassan, Sharmin Akter, et al.
Healthcare Analytics (2024) Vol. 6, pp. 100350-100350
Open Access | Times Cited: 8
Fuse-Former: An interpretability analysis model for rs-fMRI based on multi-scale information fusion interaction
Jiayu Ye, Yanting Li, An Zeng, et al.
Biomedical Signal Processing and Control (2025) Vol. 105, pp. 107471-107471
Closed Access | Times Cited: 1
Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models
Joo Hun Yoo, Harim Jeong, Ji Hyun An, et al.
Sensors (2024) Vol. 24, Iss. 2, pp. 715-715
Open Access | Times Cited: 5
CF-Net: A Hybrid CNN-Random Forest Network for Depression Classification in Brain MRI
Hui Ding, Chong Liu, Yawei Zhang, et al.
Lecture notes in computer science (2025), pp. 230-241
Closed Access
A review of detection techniques for depression and bipolar disorder
Daniel Highland, Gang Zhou
Smart Health (2022) Vol. 24, pp. 100282-100282
Closed Access | Times Cited: 22
Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning
Morteza Fattahi, Milad Esmaeil-Zadeh, Hamid Soltanian‐Zadeh, et al.
Frontiers in Human Neuroscience (2025) Vol. 18
Open Access
DepML: An Efficient Machine Learning-Based MDD Detection System in IoMT Framework
Geetanjali Sharma, Amit M. Joshi, Emmanuel S. Pilli
SN Computer Science (2022) Vol. 3, Iss. 5
Closed Access | Times Cited: 15
Federated Learning and Deep Learning Framework for MRI Image and Speech Signal-Based Multi-Modal Depression Detection
Minakshee Patil, Prachi Mukherji, Vijay M. Wadhai
Computational Biology and Chemistry (2024) Vol. 113, pp. 108232-108232
Closed Access | Times Cited: 2
The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis
Yanjing Chen, Wei Zhao, Sijie Yi, et al.
Frontiers in Neuroscience (2023) Vol. 17
Open Access | Times Cited: 6
Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram
Chetna Gupta, Vikas Khullar, Nitin Goyal, et al.
Diagnostics (2023) Vol. 14, Iss. 1, pp. 43-43
Open Access | Times Cited: 5
A novel hybrid optimization algorithm for depression detection using MRI and speech signal
Minakshee Patil, Prachi Mukherji, Vijay M. Wadhai
Biomedical Signal Processing and Control (2023) Vol. 86, pp. 105046-105046
Closed Access | Times Cited: 4
Application of Machine Learning for Image Processing in the Healthcare Sector
N. Hari Priya, Ipseeta Satpathy, B. C. M. Patnaik
Advances in medical technologies and clinical practice book series (2023), pp. 60-75
Closed Access | Times Cited: 3
Evaluation of deep learning-based depression detection using medical claims data
Markus Bertl, Nzamba Bignoumba, Peeter Ross, et al.
Artificial Intelligence in Medicine (2023) Vol. 147, pp. 102745-102745
Closed Access | Times Cited: 2
Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice
Bentley James Oakes, Michalis Famelis, Houari Sahraoui
ACM Transactions on Software Engineering and Methodology (2023) Vol. 33, Iss. 4, pp. 1-50
Open Access | Times Cited: 2
Depression Detection: Text Augmentation for Robustness to Label Noise in Self-Reports
Javed Ali, Dat Quoc Ngo, Aninda Bhattacharjee, et al.
Springer eBooks (2022), pp. 81-103
Closed Access | Times Cited: 3
Machine Learning Models to Classify and Predict Depression in College Students
Orlando Iparraguirre-Villanueva, Cleoge Paulino-Moreno, Andrés Epifanía-Huerta, et al.
International Journal of Interactive Mobile Technologies (iJIM) (2024) Vol. 18, Iss. 14, pp. 148-163
Open Access
Classification of Depression Using Machine Learning Methods Based on Eye Movement Variance Entropy
Zhongyi Jiang, Ying Zhou, Yihan Zhang, et al.
IEEE Access (2024) Vol. 12, pp. 146107-146120
Open Access
Diagnosis of major depressive disorder using a novel interpretable GCN model based on resting state fMRI
Wenhao Ma, Yu Wang, Ningxin Ma, et al.
Neuroscience (2024)
Closed Access
A Comprehensive Survey of ArtificialIntelligence in Precision Healthcare:Shedding Light on Interpretability
Nagashruthi MK, Hemanth KS, Seyed M. Buhari
Research Square (Research Square) (2024)
Open Access
A federated learning approach to classify depression using audio dataset
Chetna Gupta, Vikas Khullar
CRC Press eBooks (2024), pp. 560-564
Open Access
An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network
Shuyu Liu, Jing‐Jing Zhou, Xuequan Zhu, et al.
Patterns (2024) Vol. 5, Iss. 12, pp. 101081-101081
Open Access
An Automated MDD Detection System based on Machine Learning Methods in Smart Connected Healthcare
Geetanjali Sharma, Amit M. Joshi, Emmanuel S. Pilli
2021 IEEE International Symposium on Smart Electronic Systems (iSES) (2021), pp. 27-32
Closed Access | Times Cited: 3
Multi-classifier fusion base on belief-value for the diagnosis of neuropsychiatric disorders
Feng Zhao, Shixin Ye, Ke Lv, et al.
Research Square (Research Square) (2023)
Open Access
An Intelligent-Based System for Detecting Depression on Social Media Platform
Oluwafolake Ojo, Inioluwa Adewuyi, Oluwadolapo Oni, et al.
Research Square (Research Square) (2023)
Open Access
Machine Learning-Based Approaches in the Detection of Depression
Dipanwita Ghosh, Mihir Sing, Arpan Adhikary
Advances in medical diagnosis, treatment, and care (AMDTC) book series (2023), pp. 77-90
Closed Access
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