University Of Tasmania

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

journal contribution
posted on 2023-05-21, 02:20 authored by Alissa, M, Lones, MA, Cosgrove, J, Jane AltyJane Alty, Jamieson, S, Smith, SL, Vallejo, M
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.


Publication title

Neural Computing and Applications




Wicking Dementia Research Education Centre



Place of publication

175 Fifth Ave, New York, USA, Ny, 10010

Rights statement

Copyright The Author(s) 2021

Repository Status

  • Restricted

Socio-economic Objectives

Diagnosis of human diseases and conditions