Deep Learning-Based Diagnostic Model for Parkinson's Disease Using Handwritten Spiral and Wave Images

Abstract

Objective

To develop and validate a deep neural network (DNN) model for diagnosing Parkinson’s Disease (PD) using handwritten spiral and wave images, and to compare its performance with various machine learning (ML) and deep learning (DL) models.

Methods

The study utilized a dataset of 204 images (102 spiral and 102 wave) from PD patients and healthy subjects. The images were preprocessed using the Histogram of Oriented Gradients (HOG) descriptor and augmented to increase dataset diversity. The DNN model was designed with an input layer, three convolutional layers, two max-pooling layers, two dropout layers, and two dense layers. The model was trained and evaluated using metrics such as accuracy, sensitivity, specificity, and loss. The DNN model was compared with nine ML models (random forest, logistic regression, AdaBoost, k-nearest neighbor, gradient boost, naïve Bayes, support vector machine, decision tree) and two DL models (convolutional neural network, DenseNet-201).

Results

The DNN model outperformed all other models in diagnosing PD from handwritten spiral and wave images. On spiral images, the DNN model achieved accuracies of 41.24% over naïve Bayes, 31.24% over decision tree, and 27.9% over support vector machine. On wave images, the DNN model achieved accuracies of 40% over naïve Bayes, 36.67% over decision tree, and 30% over support vector machine. The DNN model demonstrated significant improvements in sensitivity and specificity compared to other models.

Conclusions

The DNN model significantly improves the accuracy of PD diagnosis using handwritten spiral and wave images, outperforming several ML and DL models. This approach offers a promising diagnostic tool for early PD detection and provides a foundation for future work to incorporate additional features and enhance detection accuracy.

Keywords

Parkinson’s disease / Handwritten specimens / Deep learning / Machine learning / Diagnosis accuracy

© The Author(s), under exclusive licence to Huazhong University of Science and Technology 2025

Acknowledgements We acknowledge the Vision Group on Science and Technology (VGST) for providing the funding and Nitte Meenakshi Institute of Technology for the support.

Author Contributions The author was responsible for the funding acquisition, implementation of the work and writing the original draft.

Funding This research work was supported and funded by Vision Group on Science and Technology (VGST), India having GRD number: 880.

Data Availability The data used in our research is confidential.

Declarations

Conflict of Interests The author declares that there are no competing interests.

Ethical Approval and Consent to participate Not applicable.

Human Ethics Not applicable.

Consent for Publication Not applicable.

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