An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWI
Abstrak
Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disease primarily diagnosed through diffusion-weighted imaging (DWI). However, the limitation of human visual interpretation and clinical experience can lead to inaccuracies in diagnosis. This research proposes a deep learning method based on cross self-supervision, alongside the construction of Co-ResNet50 and CO-ViT models for intelligent auxiliary diagnosis of NIID. This method uses self-supervised learning and effectively combines the characteristics of ResNet50 and ViT networks to improve the model’s feature extraction capabilities. The experiment preprocessed 249 DWI data and divided them into 204 training sets and 45 test sets. The results reveal that the CO-ResNet50 model has the best performance, with an accuracy of 95.49%, precision of 95.51%, recall of 95.44%, F1 score of 0.954 7, and AUC of 0.989 7. These findings underscore the model's potential to provide support for clinical NIID diagnosis.
Topik & Kata Kunci
Penulis (7)
CAO Fei
XU Qianqian
CHEN Hao
ZU Jie
LI Xiaowen
TIAN Jin
BAO Lei
Akses Cepat
- Tahun Terbit
- 2025
- Sumber Database
- DOAJ
- DOI
- 10.11938/cjmr20243136
- Akses
- Open Access ✓