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A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease

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Natural and Artificial Computation for Biomedicine and Neuroscience (IWINAC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10337))

Abstract

Parkinsonism is the second most common neurodegenerative disease, originated by a dopamine decrease in the striatum. Single Photon Emission Computed Tomography (SPECT) images acquired using the DaTSCAN drug are a widely extended tool in the diagnosis of Parkinson’s Disease (PD), since they can measure the amount of dopamine transporters in the striatum. Many automatic systems have been developed to aid in the diagnosis of PD, using traditional feature extraction methods. In this paper, we propose a novel system based on three-dimensional Convolutional Neural Networks (CNNs), that aims to differenciate between PD-affected patients and unaffected subjects. The proposed system achieves up to a 95.5% accuracy and 96.2% sensitivity in the diagnosis of PD.

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Acknowledgements

This work was partly supported by the MINECO/FEDER under the TEC2015-64718-R project and the Consejera de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía, Spain) under the Excellence Project P11-TIC- 7103.

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Authors and Affiliations

  1. Department of Signal Theory, Networking and Communications, University of Granada, 18071, Granada, Spain

    Francisco Jesús Martinez-Murcia, Juan Manuel Górriz, Javier Ramírez, Fermin Segovia, Diego Salas-Gonzalez & Diego Castillo-Barnes

  2. Department of Communications Engineering, University of Málaga, 29071, Málaga, Spain

    Andres Ortiz

  3. Department of Scientific Computing, The Florida State University, Tallahassee, FL, 32306-4120, USA

    Ignacio A. Illán

Authors
  1. Francisco Jesús Martinez-Murcia
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  2. Andres Ortiz
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  3. Juan Manuel Górriz
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  4. Javier Ramírez
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  5. Fermin Segovia
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  6. Diego Salas-Gonzalez
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  7. Diego Castillo-Barnes
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  8. Ignacio A. Illán
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Corresponding author

Correspondence to Francisco Jesús Martinez-Murcia .

Editor information

Editors and Affiliations

  1. Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Departamento de Inteligencia Articial, Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Departamento de Inteligencia Articial, Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain

    Javier Toledo Moreo

  5. The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

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Martinez-Murcia, F.J. et al. (2017). A 3D Convolutional Neural Network Approach for the Diagnosis of Parkinson’s Disease. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_32

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  • DOI: https://doi.org/10.1007/978-3-319-59740-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59739-3

  • Online ISBN: 978-3-319-59740-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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