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Parkinson’s disease (PD) is a progressive neurodegenerative disorder that requires early and accurate diagnosis to facilitate effective treatment and clinical management. Conven- tional clinical evaluation is generally non-specific and does not identify the disease at the onset stage. This study proposes a new method of PD prediction based on multimodal data—MRI scans and spiral drawings—and using cutting-edge deep learning methods. We used CNNs and LSTM networks for sequence pattern recognition and feature extraction, and Vision Transformers (ViTs) for high-level spatial analysis. We also investigated multimodal fusion models to fuse information from different data sources. The experiments showed that multimodal models are superior to single-modality counterparts and provide increased accuracy and robustness. This research highlights the promise of combining cutting-edge imaging technologies with deep learning to make early, non-invasive, low-cost, and scalable diagnosis of Parkinson’s disease possible, leading to AI-powered healthcare innovations.
Keywords:
Deep Learning, Convolutional Neural Networks (CNNs), Long Short-Term Mem- ory (LSTM) Networks, Pattern Detection, ResNet-18, Single-Modality Models
Cite Article:
"Predicting Parkinson’s Disease Using MRI Scans and Spiral Images: A Machine Learning Approach", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a683-a697, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507075.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator