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Assessing the Capability of Deep-Learning Models in Parkinson’s Disease Diagnosis

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Smart Multimedia (ICSM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12015))

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Abstract

Parkinson’s Disease is one of the leading age-related neurological disorders affecting the general population. Current diagnostic techniques rely on patient symptoms rather than biomarkers. Symptomatic diagnoses are subjective and can vary highly. Our work aims to remedy this by presenting a novel approach to Parkinson’s Disease diagnosis. We propose and assess four deep-learning based models that classify patients based on biomarkers found in structural magnetic resonance images, and find that our 3D-Convolution-Neural-Network model demonstrates high efficacy in the task of diagnosing Parkinson’s disease, with an accuracy of 75% and 76% sensitivity. As well, our work highlights potential biomarkers for the disease found in the cerebellum and occipital lobe.

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Acknowledgments

Special thanks to PPMI for supporting Parkinson’s disease research by maintaining and updating their clinical dataset.

Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org.

PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners found at www.ppmi-info.org/fundingpartners.

We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, AB, Canada

    Christopher West, Sara Soltaninejad & Irene Cheng

Authors
  1. Christopher West
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  2. Sara Soltaninejad
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  3. Irene Cheng
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Corresponding author

Correspondence to Christopher West .

Editor information

Editors and Affiliations

  1. Arizona State University, Mesa, AZ, USA

    Troy McDaniel

  2. University of Florence, Florence, Italy

    Stefano Berretti

  3. Nokia Technologies, Tampere, Finland

    Igor D. D. Curcio

  4. University of Alberta, Edmonton, AB, Canada

    Anup Basu

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West, C., Soltaninejad, S., Cheng, I. (2020). Assessing the Capability of Deep-Learning Models in Parkinson’s Disease Diagnosis. In: McDaniel, T., Berretti, S., Curcio, I., Basu, A. (eds) Smart Multimedia. ICSM 2019. Lecture Notes in Computer Science(), vol 12015. Springer, Cham. https://doi.org/10.1007/978-3-030-54407-2_20

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  • DOI: https://doi.org/10.1007/978-3-030-54407-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-54406-5

  • Online ISBN: 978-3-030-54407-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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