- Title
- Refining Parkinson’s neurological disorder identification through deep transfer learning
- Creator
- Naseer, Amina; Rani, Monai; Naz, Saeeda; Razzak, Muhammad; Imran, Muhammad; Xu, Guandong
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184134
- Identifier
- vital:16446
- Identifier
-
https://doi.org/10.1007/s00521-019-04069-0
- Identifier
- ISBN:0941-0643 (ISSN)
- 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. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
- Publisher
- Springer
- Relation
- Neural Computing and Applications Vol. 32, no. 3 (2020), p. 839-854
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ Springer-Verlag London Ltd
- Rights
- Open Access
- Subject
- 4602 Artificial Intelligence; 4603 Computer Vision and Multimedia Computation; 4611 Machine Learning; Handwriting analysis; Neurodegenerative disorder; Parkinson disease
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