Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data
The obtained results highlight exciting new prospects for the use of handwriting as a biomarker, along with other information, for optimal model performance. PMMD's success in integrating diverse data sources through cross-modal attention underscores its potential as a robust diagnostic decision support tool for accurately diagnosing PD.
Authors
- Rawad Abdulghafor (ORCID: https://orcid.org/0000-0002-0266-1027)
- Sherzod Turaev (ORCID: https://orcid.org/0000-0001-6661-8469)
- Akram Bennour (ORCID: https://orcid.org/0000-0002-3185-8806)
- Mohammed Al-Sarem (ORCID: https://orcid.org/0000-0001-7172-8224)
- Abdulaziz Aborujilah (ORCID: https://orcid.org/0000-0002-7497-4321)
- Tahar Mekhaznia (ORCID: https://orcid.org/0000-0002-4791-4227)
- Sabrina Benredjem
- Bourmatte Sofiane
Institutions
- Arab Open University (OM)
- United Arab Emirates University (AE)
- Université Larbi Tébessi (DZ)
- Yemenia University (YE)
- Dhofar University (OM)
Publication Details
- Journal
- Diagnostics
- Published
- 2024-12-24
- DOI
- https://doi.org/10.3390/diagnostics15010004
- Citations
- 17
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 4.21
Funders
- United Arab Emirates University