We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A coordinate attention enhanced swin transformer for handwriting recognition of Parkinson's disease.
- Authors
Wang, Nana; Niu, Xuesen; Yuan, Yiyang; Sun, Yunze; Li, Ran; You, Guoliang; Zhao, Aite
- Abstract
Diagnosing Parkinson's disease (PD) in its early stages is a significant challenge in medicine. Hand tremors and dysgraphia, which are typical early motor symptoms of PD, can manifest for decades before a formal diagnosis is made. Therefore, handwriting analysis has become an important tool for detecting PD. While many machine learning algorithms have been applied in this area, they struggle to capture the subtle changes in handwriting and must describe features from various perspectives. To address these issues, this paper proposes a Coordinate Attention Enhanced Swin Transformer (CAS Transformer) model for PD handwriting recognition. It establishes the long‐term dependence of features on the joint coordinate attention application, which enables the model to more accurately localize the important features of handwriting data and also extract the fuzzy edge features of handwriting images.These characteristics of the CAS Transformer enable it to outperform current advanced deep learning methods in classification, with an accuracy of 92.68% in experiments conducted on two handwritten datasets.
- Subjects
PARKINSON'S disease; MACHINE learning; GRAPHOLOGY; HANDWRITING; DEEP learning; COORDINATES; IMAGE recognition (Computer vision); HILBERT transform
- Publication
IET Image Processing (Wiley-Blackwell), 2023, Vol 17, Issue 9, p2686
- ISSN
1751-9659
- Publication type
Article
- DOI
10.1049/ipr2.12820