Innovative Deep Learning Approach for Parkinson's Disease Prediction: Leveraging Convolutional Neural Networks for Early Detection


  • Bhagyashri R. Wankar G.H. Raisoni College of Engineering and Management
  • Nikita V. Kshirsagar G.H. Raisoni College of Engineering and Management
  • Amisha V. Jadhav G.H. Raisoni College of Engineering and Management
  • Srushti R. Bawane G.H. Raisoni College of Engineering and Management
  • Shubham M. Koshti G.H. Raisoni College of Engineering and Management



Parkinson Disease, Healthy Control, Convolutional Neural Network, MRI, Deep Learning


INTRODUCTION: Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting movement control, highlighting the importance of timely detection and intervention to improve patient quality of life. However, accurate diagnosis remains challenging due to its similarity with other neurological conditions, leading to a 25% rate of inaccurate manual diagnoses. Convolutional Neural Networks (CNNs) offer a promising solution for medical image classification and analysis, capable of learning complex patterns in images. In this study, we introduce an innovative automated diagnostic model using CNN that gives an appropriate output about if the person is diagnosed with PD or not.

OBJECTIVES: The study aims to develop an automated diagnostic model using CNNs to accurately diagnose PD. By leveraging the Parkinson Progression Markers Initiative (PPMI) dataset, which provides benchmarked MRI images of PD and healthy controls, the model seeks to differentiate between PD and non-PD cases.

METHODS: A Convolutional Neural Network (CNN) is a deep learning algorithm that is suitable for medical image classification and analysis as they are able to learn complex patterns in images and identify the hidden patterns and trend of data. We have used VGG16 and ResNet50 pretrained CNN models to achieve high accuracy and prediction.

RESULTS: These models collectively achieved an outstanding accuracy rate of 97%. To validate our model performance, we test our model by applying various algorithms and activation functions such as EfficientNetB0, EfficientNetB1 and softmax, sigmoid, and ReLu respectively.

CONCLUSION: This research introduces an innovative framework for the early detection of Parkinson’s disease using convolutional neural networks. Our system demonstrates remarkable capability to identify subtle patterns indicative of PD in its early stages.


Download data is not yet available.


. P. M. Shah, A. Zeb, U. Shafi, S. F. A. Zaidi and M. A. Shah, "Detection of Parkinson Disease in Brain MRI using Convolutional Neural Network," 2018 24th International Conference on Automation and Computing (ICAC), Newcastle Upon Tyne, UK, 2018, pp. 1-6, doi: 10.23919/IConAC.2018.8749023. DOI:

. S. Marar, D. Swain, V. Hiwarkar, N. Motwani and A. Awari, "Predicting the occurrence of Parkinson’s Disease using various Classification Models," 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), Bhopal, India, 2018, pp. 1-5, doi: 10.1109/ICACAT.2018.8933579. DOI:

. Solana-Lavalle, Gabriel & Galan-Hernandez, J. & Rosas-Romero, Roberto. “Automatic Parkinson disease detection at early stages as a pre-diagnosis tool by using classifiers and a small set of vocal features. Biocybernetics and Biomedical Engineering”. ResearchGate, (2020). DOI:

. Sonavane, S. M., Prashantha, G. R., Deshmukh, J. Y., Salunke, M. D., Jadhav, H. B., & Nikam, P. D. (2023). Design of a Blockchain-Based Access Control Model with QoS-Awareness Via Bioinspired Computing Techniques. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 631-639.

. W. Wang, J. Lee, F. Harrou and Y. Sun, "Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning," in IEEE Access, vol. 8, pp. 147635-147646, 2020, doi: 10.1109/ACCESS.2020.3016062. DOI:

. Alshammri, R., Alharbi, G., Alharbi, E., & Almubark, I. (2023). Machine learning approaches to identify Parkinson's disease using voice signal features. Frontiers in Artificial Intelligence, 6, 1084001. doi:10.3389/frai.2023.1084001 DOI:

. F. Amato, I. Rechichi, L. Borzì and G. Olmo, "Sleep Quality through Vocal Analysis: a Telemedicine Application," 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Pisa, Italy, 2022, pp. 706-711, doi: 10.1109/PerComWorkshops53856.2022.9767372. DOI:

. Prabhavathi, K., Patil, S. (2022). Tremors and Bradykinesia. In: Arjunan, S.P., Kumar, D.K. (eds) Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation. Series in BioEngineering. Springer, Singapore. DOI:

. Alatas, B., Moradi, S., Tapak, L., & Afshar, S. (2022). Identification of Novel Noninvasive Diagnostics Biomarkers in Parkinson’s Disease and Improving Disease Classification Using Support Vector Machine. BioMed Research International, 2022, 5009892. DOI:

. F. Cordella, A. Paffi and A. Pallotti, "Classification-based screening of Parkinson’s disease patients through voice signal," 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Lausanne, Switzerland, 2021, pp. 1-6, doi: 10.1109/MeMeA52024.2021.9478683. DOI:

. F. Huang, H. Xu, T. Shen and L. Jin, "Recognition of Parkinson's Disease Based on Residual Neural Network and Voice Diagnosis," 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Xi'an, China, 2021, pp. 381-386, doi: 10.1109/ITNEC52019.2021.9586915. DOI:

. M. Wodzinski, A. Skalski, D. Hemmerling, J. R. Orozco-Arroyave and E. Nöth, "Deep Learning Approach to Parkinson’s Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 717-720, doi: 10.1109/EMBC.2019.8856972. DOI:

. T. J. Wroge, Y. Özkanca, C. Demiroglu, D. Si, D. C. Atkins and R. H. Ghomi, "Parkinson’s Disease Diagnosis Using Machine Learning and Voice," 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA, 2018, pp. 1-7, doi: 10.1109/SPMB.2018.8615607. DOI:

. R. Alkhatib, M. O. Diab, C. Corbier and M. E. Badaoui, "Machine Learning Algorithm for Gait Analysis and Classification on Early Detection of Parkinson," in IEEE Sensors Letters, vol. 4, no. 6, pp. 1-4, June 2020, Art no. 6000604, doi: 10.1109/LSENS.2020.2994938. DOI:

. C. Ricciardi et al., "Machine learning can detect the presence of Mild cognitive impairment in patients affected by Parkinson’s Disease," 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Bari, Italy, 2020, pp. 1-6, doi: 10.1109/MeMeA49120.2020.9137301. DOI:

. X. Yang, Q. Ye, G. Cai, Y. Wang and G. Cai, "PD-ResNet for Classification of Parkinson’s Disease From Gait," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1-11, 2022, Art no. 2200111, doi: 10.1109/JTEHM.2022.3180933. DOI:

. A. U. Haq et al., "Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings," in IEEE Access, vol. 7, pp. 37718-37734, 2019, doi: 10.1109/ACCESS.2019.2906350. DOI:

. Jie, M., Desrosiers, C., & Frasnelli, J. (2021). Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature. Frontiers in Aging Neuroscience, 13, Article 633752. DOI:




How to Cite

R. Wankar B, V. Kshirsagar N, V. Jadhav A, R. Bawane S, M. Koshti S. Innovative Deep Learning Approach for Parkinson’s Disease Prediction: Leveraging Convolutional Neural Networks for Early Detection. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 May 29 [cited 2024 Jul. 13];10. Available from: