Deep Learning for Parkinson’s Disease Detection: A Review of Recent Advances and Challenges

DOI

10.2991/978-94-6239-768-2_2How to use a DOI?

Keywords

Deep Learning; Recent Advances; Convolutional Neural Networks; Parkinson’s disease

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects both motor function and cognitive ability, leading to symptoms such as tremors, rigidity, bradykinesia, and cognitive decline. Early and accurate diagnosis is essential for effective intervention and to slow disease progression. However, conventional diagnostic methods are often subjective, reliant on clinical observation, and prone to variability between practitioners. In recent years, deep learning (DL) has emerged as a transformative tool in medical imaging and pattern recognition, offering promising results in the automated detection of PD. Specifically, Convolutional Neural Networks (CNNs) have demonstrated superior performance in analyzing diverse data modalities, including voice recordings, gait dynamics, handwriting samples, and neuroimaging scans such as MRI and PET. This review provides an overview of recent developments in CNN-based approaches for PD detection, highlighting their advantages over traditional methods. Furthermore, it critically examines key challenges such as data heterogeneity, limited dataset availability, computational constraints, and ethical considerations related to patient data privacy and algorithmic transparency. By addressing these limitations, the paper proposes a roadmap for future research and deployment of DL-based diagnostic systems aimed at improving the reliability and accessibility of PD detection in clinical practice.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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TY  - CONF
AU  - Abdulaziz Salihu Aliero
AU  - Neha Malhotra
PY  - 2026
DA  - 2026/09/07
TI  - Deep Learning for Parkinson’s Disease Detection: A Review of Recent Advances and Challenges
BT  - Proceedings of the Third International Conference on Recent Advances in Computing Sciences (RACS 2025)
PB  - Atlantis Press
SP  - 6
EP  - 18
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-768-2_2
DO  - 10.2991/978-94-6239-768-2_2
ID  - Aliero2026
ER  -