Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks

Mohamad Alissa, Michael Adam Lones, Jeremy Cosgrove, Jane E. Alty, Stuart Jamieson, Stephen L. Smith, Marta Vallejo

Research output: Contribution to journalArticlepeer-review

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. This work aims to contribute to the PD diagnosis process by using a convolutional neural network, a type of deep neural network architecture, to differentiate between healthy controls and PD patients. Our approach focuses on discovering deviations in patient’s movements with the use of drawing tasks. In addition, this work explores which of two drawing tasks, wire cube or spiral pentagon, are more effective in the discrimination process. With 93.5 % accuracy, our convolutional classifier, trained with images of the pentagon drawing task and augmentation techniques, can be used as an objective method to discriminate PD from healthy controls. Our compact model has the potential to be developed into an offline real-time automated single-task diagnostic tool, which can be easily deployed within a clinical setting.

Original languageEnglish
Number of pages22
JournalNeural Computing and Applications
DOIs
Publication statusPublished - 8 Sep 2021

Keywords

  • Convolutional neural networks
  • Deep learning classifier
  • Diagnosis
  • Drawing tasks
  • Parkinson’s disease

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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