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HSNet:一个基于喉镜语音多式数据的自适应融合网络,用于喉疾病分类

Mei Wei1, Xiu Zhang2, Lei Geng2

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, 300192, China; Institute of Otolaryngology of Tianjin, Tianjin, China; Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China; Key Clinical Discipline of Tianjin (Otolaryngology), Tianjin, China; Otolaryngology Clinical Quality Control Centre, Tianjin, China.

American journal of otolaryngology
|October 28, 2025
PubMed
概括

这项研究引入了一个深度学习模型,将喉腔镜图像和语音数据结合起来,用于准确的喉疾病诊断. 多模式方法的准确率达到了87.92%,超过单模式方法的性能.

关键词:
喉疾病的分类 喉疾病的分类层次化的特征融合融合.喉镜镜像图像 喉镜像图像多模式学习是多模式学习.语音信号是一个声音信号.

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