Composite nystagmus classification framework enhanced by dual attention mechanism(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2024年第9期
- Page:
- 1093-1103
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Composite nystagmus classification framework enhanced by dual attention mechanism
- Author(s):
- WANG Zhuoran; FANG Zhijun; WANG Hailing; GAO Yongbin; LI Yuxia
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201600, China
- Keywords:
- Keywords: medical image processing videonystagmography benign paroxysmal positional vertigo deep learning attention mechanism
- PACS:
- R318；TP391
- DOI:
- DOI:10.3969/j.issn.1005-202X.2024.09.006
- Abstract:
- Abstract: A composite nystagmus classification framework enhanced by dual attention mechanism is proposed to address the problem that the existing researches only identify whether nystagmus occurs in a horizontal, vertical, or axial direction, but fail to consider the issue of composite nystagmus composed of multiple directions with various intensities in clinical practice. A spatiotemporal concentration algorithm for nystagmus videos is presented, and it combines convolutional neural networks and Hough transform to remove interference from invalid frames and regions and to improve the quality of nystagmus videos. Then, a dense optical flow algorithm is used to extract the optical flow field of eye movement. Finally, a composite nystagmus classification network based on dual attention mechanism enhancement is constructed. An improved efficient channel attention module is used to effectively obtain the direction and intensity of nystagmus in different channels of the optical flow map and a temporal attention module is added at the end of the bidirectional long short-term memory network to achieve significant expression of classification results based on different temporal features. On the nystagmus dataset provided by the cooperating hospital, the proposed method has an accuracy rate of 83.17% for composite nystagmus classification, and achieved accuracy rates of 91.03%, 89.74%, and 86.05% for individual horizontal, vertical, and axial nystagmus classifications. The proposed method realizes the intelligent classification of composite nystagmus and is valuable in clinic.
Last Update: 2024-09-26