An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery

Magesh, Pavan Rajkumar; Myloth, Richard Delwin; Tom, Rijo Jackson

doi:10.1016/j.compbiomed.2020.104041

Cited by 296Explainable Artificial Intelligence (XAI)Artificial Intelligence in HealthcareParkinson's Disease Mechanisms and Treatments

An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery

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Magesh, Pavan Rajkumar, Myloth, Richard Delwin, Tom, Rijo Jackson·2020·Computers in Biology and Medicine

DOI 10.1016/j.compbiomed.2020.104041

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Abstract

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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Authors: Magesh, P. R., Myloth, R. D., & Tom, R. J.
Affiliations: CMR University (IN)
Corresponding author: Tom, Rijo Jackson
ORCID: Myloth, Richard Delwin
Tom, Rijo Jackson
Venue: Computers in Biology and Medicine
Publisher: Elsevier BV
Published: 2020-11
Volume: 126
Pages: 104041
ISSN: 0010-4825 (ISSN-L)
DOI: 10.1016/j.compbiomed.2020.104041
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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 › 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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An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery

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