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An Explainable Approach for Early Parkinson Disease Detection Using Deep Learning

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Deep Learning Theory and Applications (DeLTA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1875))

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Abstract

Parkinson’s disease (PD) is a progressive disorder that affects the nervous system and all the parts of the body controlled by it. It is the second most diffused neurodegenerative disorder, showing increasing trends in the last years and requiring new tools and procedures for diagnosis and assessment. In order to be used in medical clinics, the PD detection approaches require high effectiveness in disease detection and good capability to drive the experts in the comprehension and checking of the prediction’s reasons. According to this, this paper proposes an explainable Deep Learning approach for the detection of PD from single photon emission computed tomography (SPECT) images. The approach consists of a combination of a CNN prediction model and a Gradient weighted Class Activation Mapping (Grad-CAM) interpretable technique. The validation is performed on a known dataset belonging to Parkinson’s Progression Markers Initiative (PPMI). For this dataset, SPECT images of 974 patients are used showing good accuracy in the classification of healthy and PD patients and a good capability to explain the obtained prediction.

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Notes

  1. 1.

    www.ppmi-info.org.

  2. 2.

    https://autonomio.github.io/talos.

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Author information

Authors and Affiliations

  1. Department of Engineering, University of Sannio, Benevento, Italy

    Lerina Aversano, Mario L. Bernardi, Martina Iammarino, Antonella Madau & Chiara Verdone

  2. Unitelma Sapienza University, Rome, Italy

    Marta Cimitile

Authors
  1. Lerina Aversano
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  2. Mario L. Bernardi
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  3. Marta Cimitile
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  4. Martina Iammarino
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  5. Antonella Madau
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  6. Chiara Verdone
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Corresponding author

Correspondence to Lerina Aversano .

Editor information

Editors and Affiliations

  1. Université de Tours, Tours, France

    Donatello Conte

  2. Instituto de Telecomunicações and University of Lisbon, Lisbon, Portugal

    Ana Fred

  3. Ford Motor Company, Commerce Township, MI, USA

    Oleg Gusikhin

  4. University of Naples Federico II, Naples, Italy

    Carlo Sansone

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Aversano, L., Bernardi, M.L., Cimitile, M., Iammarino, M., Madau, A., Verdone, C. (2023). An Explainable Approach for Early Parkinson Disease Detection Using Deep Learning. In: Conte, D., Fred, A., Gusikhin, O., Sansone, C. (eds) Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science, vol 1875. Springer, Cham. https://doi.org/10.1007/978-3-031-39059-3_22

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  • DOI: https://doi.org/10.1007/978-3-031-39059-3_22

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  • Publisher Name: Springer, Cham

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