Thus, the model based on attention mechanisms exhibits better classification performance than existing methods for grading bladder prolapse in pelvic organs, and it can effectively assist physicians in achieving a more accurate bladder prolapse diagnosis.
Keyphrases
- spinal cord injury
- deep learning
- working memory
- rectal cancer
- urinary tract
- primary care
- urinary incontinence
- magnetic resonance imaging
- machine learning
- convolutional neural network
- contrast enhanced
- high resolution
- optical coherence tomography
- computed tomography
- magnetic resonance
- mass spectrometry
- energy transfer