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An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery
Open accessAbstract
Parkinson's Disease (PD) is a degenerative and progressive neurological condition. Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTSCAN. In this study, we propose a machine learning model that accurately classifies any given DaTSCAN as having Parkinson's disease or not, in addition to providing a plausible reason for the prediction. This kind of reasoning is done through the use of visual indicators generated using Local Interpretable Model-Agnostic Explainer (LIME) methods. DaTSCANs were drawn from the Parkinson's Progression Markers Initiative database and trained on a CNN (VGG16) using transfer learning, yielding an accuracy of 95.2%, a sensitivity of 97.5%, and a specificity of 90.9%. Keeping model interpretability of paramount importance, especially in the healthcare field, this study utilises LIME explanations to distinguish PD from non-PD, using visual superpixels on the DaTSCANs. It could be concluded that the proposed system, in union with its measured interpretability and accuracy may effectively aid medical workers in the early diagnosis of Parkinson's Disease.
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“However, surrogate models may only accurately approximate the original model in specific local regions [22].”
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Metadata
- Authors
- Magesh, P. R., Myloth, R. D., & Tom, R. J.
- Affiliations
- CMR University (IN)
- Corresponding author
- Tom, Rijo Jackson
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 126
- Pages
- 104041
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W3046695717
- Cited by
- 295
- References
- 40
- Citations by year
- 2026: 35 · 2025: 75 · 2024: 67 · 2023: 59 · 2022: 41 · 2021: 16 · 2020: 1
- FWCI
- 11.50
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Clinical Neurology › Parkinson's Disease Mechanisms and Treatments
- Keywords
- Parkinson's disease, early diagnosis, DaTSCAN, SPECT imaging, lime, explainable machine learning, model interpretability, VGG16, transfer learning, Parkinson's Progression Markers Initiative, medical image classification
- MeSH terms
- Machine Learning, Dopamine, Humans, Parkinson Disease, Tomography, Emission-Computed, Single-Photon, Early Diagnosis
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