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Original Article
Multimodal Deep Learning Approach for Parkinson’s Disease Recognition
Dr.Sayyad Rasheeduddin1
K. Sai Krishna2
B. Ganesh3
S. Arun4
1 Associate Professor, Department of CSE (AI & ML), CMR Engineering College, Hyderabad, Telangana, India. 2 3 4 Research Scholar, Department of CSE (AI & ML), CMR Engineering College, Hyderabad, Telangana, India.
Published Online: May-June 2026
Pages: 283-289
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260603032
References
1. A. H. Behroozi, A. Nosratighods, and M. G. Shayesteh, “Diagnosis of PD Using Deep Neural Network Classifier,” Journal of Biomedical Informatics, vol. 98, pp. 103-112, 2021.
2. S. Prashanth, R. Dutta Roy, P. Mandal, and S. Ghosh, “Predicting Seriousness of PD Using Deep Learning,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 8, pp. 1448-1456, 2018.
3. J. W. Fisher, M. Z. Khan, and A. G. Jacob, “Deep Learning-Aided Parkinson’s Disease Diagnosis from Handwritten Dynamics,” Expert Systems along with Applications, vol. 176, pp. 113-121, 2022.
4. M. E. Ashtari, R. Maleki, and A. J. Tarokh, “Refining Diagnosis of Parkinson’s Disease with Deep Learning-Based Interpretation of Dopamine Transporter Imaging, 2021.
5. J. Q. Zhangu, K. H. Wong, and C. Y. Lau, “End-to-End Parkinson Disease Diagnosis Using Brain MRI Images by 3D-CNN,” Neurocomputing, vol. 403, pp. 49-90, 2020.
6. B. Wang, Y. Liu, and X. Wang, “Analysis and Identification of Parkinson’s Disease Based on fMRI,” International Journal of Imaging Systems and Technology, vol. 31, no. 3, pp. 521-532, 2021.
7. H. T. Nguyen, P. C. Le, and T. H. Tran, “Prediction of Parkinson’s Disease Tumor On Using a Radial Function Neural Network Based on Particle Swarm Optimization,” Applied Soft Computing, vol. 100, pp. 107-129, 2022.
8. UCI Machine Learning Repository, “Parkinson’s Telemonitoring Voice Data Set,” University of California, Irvine, 2009. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Parkinsons
9. Physio Network, “Gait in Parkinson’s Disease Database,” MIT Laboratory for Computational Physiology, 2019. [Online]. Available: https://physioonet.org/content/gaitpdb/
10. World Health Organization (WHO), “Neurological Disorders: Public Health Challenges,” WHO Reports, 2020.[Online]Available: https://www.who.int/publications-detail/neurological-disorders
11. P. Martinez Martin, W. A. Chaudhuri, and L. Rodriguez-Blazquez, “The Impact of Parkinson’s Disease Health Costs: A Global Perspective,” Journal of Parkinson’s Disease, vol. 9, no. 3, pp. 530-542, 2021.
12. K. P. Bhatt, M. A. Grosman, and J. F. Bender, “Machine Learning Based Parkinson’s Disease Detection Using Voice Data: A Review,” Biomedical Signal Processing and Control, vol. 68, pp. 102-120, 2022. Image and Vision Computing, vol. 15(8), pp. 646–654, (1999).
2. S. Prashanth, R. Dutta Roy, P. Mandal, and S. Ghosh, “Predicting Seriousness of PD Using Deep Learning,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 8, pp. 1448-1456, 2018.
3. J. W. Fisher, M. Z. Khan, and A. G. Jacob, “Deep Learning-Aided Parkinson’s Disease Diagnosis from Handwritten Dynamics,” Expert Systems along with Applications, vol. 176, pp. 113-121, 2022.
4. M. E. Ashtari, R. Maleki, and A. J. Tarokh, “Refining Diagnosis of Parkinson’s Disease with Deep Learning-Based Interpretation of Dopamine Transporter Imaging, 2021.
5. J. Q. Zhangu, K. H. Wong, and C. Y. Lau, “End-to-End Parkinson Disease Diagnosis Using Brain MRI Images by 3D-CNN,” Neurocomputing, vol. 403, pp. 49-90, 2020.
6. B. Wang, Y. Liu, and X. Wang, “Analysis and Identification of Parkinson’s Disease Based on fMRI,” International Journal of Imaging Systems and Technology, vol. 31, no. 3, pp. 521-532, 2021.
7. H. T. Nguyen, P. C. Le, and T. H. Tran, “Prediction of Parkinson’s Disease Tumor On Using a Radial Function Neural Network Based on Particle Swarm Optimization,” Applied Soft Computing, vol. 100, pp. 107-129, 2022.
8. UCI Machine Learning Repository, “Parkinson’s Telemonitoring Voice Data Set,” University of California, Irvine, 2009. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Parkinsons
9. Physio Network, “Gait in Parkinson’s Disease Database,” MIT Laboratory for Computational Physiology, 2019. [Online]. Available: https://physioonet.org/content/gaitpdb/
10. World Health Organization (WHO), “Neurological Disorders: Public Health Challenges,” WHO Reports, 2020.[Online]Available: https://www.who.int/publications-detail/neurological-disorders
11. P. Martinez Martin, W. A. Chaudhuri, and L. Rodriguez-Blazquez, “The Impact of Parkinson’s Disease Health Costs: A Global Perspective,” Journal of Parkinson’s Disease, vol. 9, no. 3, pp. 530-542, 2021.
12. K. P. Bhatt, M. A. Grosman, and J. F. Bender, “Machine Learning Based Parkinson’s Disease Detection Using Voice Data: A Review,” Biomedical Signal Processing and Control, vol. 68, pp. 102-120, 2022. Image and Vision Computing, vol. 15(8), pp. 646–654, (1999).
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