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

  • Francisco Jesús Martinez-MurciaEmail author
  • Andres Ortiz
  • Juan Manuel Górriz
  • Javier Ramírez
  • Fermin Segovia
  • Diego Salas-Gonzalez
  • Diego Castillo-Barnes
  • Ignacio A. Illán
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10337)


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.


Single Photon Emission Compute Tomography Progressive Supranuclear Palsy Independent Component Analysis Convolutional Neural Network Progressive Supranuclear Palsy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Francisco Jesús Martinez-Murcia
    • 1
    Email author
  • Andres Ortiz
    • 2
  • Juan Manuel Górriz
    • 1
  • Javier Ramírez
    • 1
  • Fermin Segovia
    • 1
  • Diego Salas-Gonzalez
    • 1
  • Diego Castillo-Barnes
    • 1
  • Ignacio A. Illán
    • 3
  1. 1.Department of Signal Theory, Networking and CommunicationsUniversity of GranadaGranadaSpain
  2. 2.Department of Communications EngineeringUniversity of MálagaMálagaSpain
  3. 3.Department of Scientific ComputingThe Florida State UniversityTallahasseeUSA

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