Early Detection of Parkinson’s Disease Using Automatic Classification of Single-photon Emission Computed Tomography Images
Jihad Boucherouite, Abdelilah Jilbab, Atman Jbari · 2026
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Description
Boucherouite and colleagues built a 2026 automated system to spot early Parkinson's disease from brain scans called DaTSCAN images, which show dopamine activity. Visual reading of these scans can miss early disease. The team used 514 scans from a large Parkinson's research database, one per person.
Their pipeline processed each image, computed 23 features describing shape and tracer uptake, and classified scans with machine learning. A support vector machine reached about 97 percent accuracy, catching nearly all true cases while keeping false alarms low. It beat a deep learning baseline, which scored about 95 percent, and it trained much faster. The authors argue that careful feature design plus classical methods can suit clinics better when training data are limited.
Catalog record
| CREATOR | Jihad Boucherouite, Abdelilah Jilbab, Atman Jbari |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 29 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |