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Automated Classification of Parkinson’s Disease Using Diffusion Tensor Imaging Data

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12510))

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Abstract

Parkinson’s Disease (PD) is one of the most common neurological disorders in the world, affecting over 6 million people globally. In recent years, Diffusion Tensor Imaging (DTI) biomarkers have been established as one of the leading techniques to help diagnose the disease. However, identifying patterns and deducing even preliminary results require a neurologist to automatically analyze the scan. In this paper, we propose a Machine Learning (ML) based algorithm that can analyze DTI data and predict if the person has PD. We were able to obtain a classification accuracy of 80% and an F1 score of 0.833 using our approach. The method proposed is expected to reduce the number of misdiagnosis by assisting the neurologists in making a decision.

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Authors and Affiliations

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

    Harsh Sharma, Sara Soltaninejad & Irene Cheng

Authors
  1. Harsh Sharma
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  2. Sara Soltaninejad
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  3. Irene Cheng
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Corresponding author

Correspondence to Harsh Sharma .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Sharma, H., Soltaninejad, S., Cheng, I. (2020). Automated Classification of Parkinson’s Disease Using Diffusion Tensor Imaging Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_52

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  • DOI: https://doi.org/10.1007/978-3-030-64559-5_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64558-8

  • Online ISBN: 978-3-030-64559-5

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