Refining Parkinson’s neurological disorder identification through deep transfer learning

Amina NASEER, Monail RANI, Saeeda NAZ, Muhammad Imran RAZZAK, Muhammad IMRAN, Guandong XU

Research output: Contribution to journalArticlespeer-review

104 Citations (Scopus)

Abstract

Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work. Copyright © 2019 Springer-Verlag London Ltd., part of Springer Nature.

Original languageEnglish
Pages (from-to)839-854
JournalNeural Computing and Applications
Volume32
Early online dateFeb 2019
DOIs
Publication statusPublished - Feb 2020

Citation

Naseer, A., Rani, M., Naz, S., Razzak, M. I., Imran, M., & Xu, G. (2020). Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Computing and Applications, 32, 839-854. https://doi.org/10.1007/s00521-019-04069-0

Keywords

  • Parkinson disease
  • Handwriting analysis
  • Neurodegenerative disorder

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