Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Explainable deep learning architecture for early diagnosis of Parkinson’s disease
Yi‐Wei Ma, Jiann-Liang Chen, Yan-Ju Chen, et al.
Soft Computing (2021) Vol. 27, Iss. 5, pp. 2729-2738
Closed Access | Times Cited: 21

Showing 21 citing articles:

Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson’s disease
Muhammad Junaid, Sajid Ali, Fatma Eid, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 234, pp. 107495-107495
Closed Access | Times Cited: 45
Parkinson’s Disease Detection Using Hybrid LSTM-GRU Deep Learning Model
Amjad Rehman, Tanzila Saba, Muhammad Mujahid, et al.
Electronics (2023) Vol. 12, Iss. 13, pp. 2856-2856
Open Access | Times Cited: 32
Parkinson’s disease diagnosis using deep learning: A bibliometric analysis and literature review
Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Sarminah Samad, et al.
Ageing Research Reviews (2024) Vol. 96, pp. 102285-102285
Closed Access | Times Cited: 11
XAI–reduct: accuracy preservation despite dimensionality reduction for heart disease classification using explainable AI
Surajit Das, Mahamuda Sultana, S. D. Bhattacharya, et al.
The Journal of Supercomputing (2023) Vol. 79, Iss. 16, pp. 18167-18197
Open Access | Times Cited: 19
An efficient tool for Parkinson's disease detection and severity grading based on time-frequency and fuzzy features of cumulative gait signals through improved LSTM networks
Farhad Abedinzadeh Torghabeh, Yeganeh Modaresnia, Seyyed Abed Hosseini
Medicine in Novel Technology and Devices (2024) Vol. 22, pp. 100297-100297
Open Access | Times Cited: 6
Enhancing Parkinson’s Disease Diagnosis through Stacking Ensemble-Based Machine Learning Approach
Riyadh M. Al-Tam, Fatma A. Hashim, Sarmad Maqsood, et al.
IEEE Access (2024) Vol. 12, pp. 79549-79567
Open Access | Times Cited: 6
Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning
Emrullah Şahin, Naciye Nur Arslan, Durmuş Özdemir
Neural Computing and Applications (2024)
Closed Access | Times Cited: 6
Explainable machine learning with pairwise interactions for the classification of Parkinson’s disease and SWEDD from clinical and imaging features
Alessia Sarica, Andrea Quattrone, Aldo Quattrone
Brain Imaging and Behavior (2022) Vol. 16, Iss. 5, pp. 2188-2198
Open Access | Times Cited: 22
Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review
Huan Zhao, Junyi Cao, Junxiao Xie, et al.
Digital Health (2023) Vol. 9
Open Access | Times Cited: 14
A survey of artificial intelligence in gait-based neurodegenerative disease diagnosis
Haocong Rao, Minlin Zeng, Xuejiao Zhao, et al.
Neurocomputing (2025), pp. 129533-129533
Closed Access
The Role of Deep Learning and Gait Analysis in Parkinson’s Disease: A Systematic Review
Alessandra Franco, Michela Russo, Marianna Amboni, et al.
Sensors (2024) Vol. 24, Iss. 18, pp. 5957-5957
Open Access | Times Cited: 3
A new Diagnosis using a Parkinson's Disease XGBoost and CNN-based classification model Using ML Techniques
Anil Kumar N, Bhavini Rajendrakumar Bhatt, P. Anitha, et al.
(2022)
Closed Access | Times Cited: 14
Mahalanobis Metric-based Oversampling Technique for Parkinson’s Disease Severity Assessment using Spatiotemporal Gait Parameters
Aishwarya Balakrishnan, Jeevan Medikonda, Pramod K. Namboothiri, et al.
Biomedical Signal Processing and Control (2023) Vol. 86, pp. 105057-105057
Closed Access | Times Cited: 7
Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in Non-IID Datasets
Ying-Hsun Lai, Shih-Yeh Chen, Wen‐Chi Chou, et al.
Future Internet (2024) Vol. 16, Iss. 3, pp. 95-95
Open Access | Times Cited: 1
Enhancing Parkinson's Disease Diagnosis through Deep Learning-Based Classification of 3D MRI Images
Shivani Desai, Hitesh Chhinkaniwala, Smit Shah, et al.
Procedia Computer Science (2024) Vol. 235, pp. 201-213
Open Access | Times Cited: 1
An art painting style explainable classifier grounded on logical and commonsense reasoning
Vicent Costa, José M. Alonso, Zoe Falomir, et al.
Soft Computing (2023)
Open Access | Times Cited: 2
Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in non-IID Datasets
Ying-Hsun Lai, S. Chen, Wen Chi Chou, et al.
(2024)
Open Access
CNN-Based Approach for Accurate Parkinson’s Disease Detection from Gait Analysis Data
Eugenia TÎRZIU, Ana-Mihaela Vasilevschi, Eleonora Tudora, et al.
IFMBE proceedings (2024), pp. 737-745
Closed Access
Exploring Advanced Deep Learning Paradigms for Precise Brain Tumor Categorization
Daisy Elizabeth Imbaquingo Esparza, Miguel Botto-Tobar, José G. Jacome-Leon, et al.
SN Computer Science (2024) Vol. 5, Iss. 7
Closed Access
Forecast the Onset of Parkinson’s Disease at all Three Stages using Deep Learning Techniques
Caroline El Fiorenza J, V. Sellam, J. Dafni Rose
(2023), pp. 1-8
Closed Access | Times Cited: 1
An Empirical Comparison among Supervised Learning Algorithms with Model Explainability
Syeda Somiya Tasnim, Mohammad Kawser Jamal, Subrina Akter, et al.
(2023), pp. 1-6
Closed Access
Page 1

Scroll to top