通过使用1D-CNN的肺声分类来识别呼吸系统疾病的临床决策支持系统的发展
Syed Waqad Ali1,2, Muhammad Munaf Rashid1, Muhammad Uzair Yousuf3
1Data Acquisition, Processing & Predictive Analytics Lab, National Center in Big Data and Cloud Computing (NCBC), Ziauddin University, Karachi 74600, Pakistan.
本研究介绍了一种人工智能驱动的系统,用于使用1D-CNN模型早期检测呼吸系统疾病. 该系统准确地分类肺部声音,改善诊断和患者护理.
科学领域:
- 人工智能在医学中的应用
- 呼吸系统医学 呼吸系统医学
- 机器学习用于医疗保健
背景情况:
- 由于多因素的原因,呼吸系统疾病的诊断是复杂的.
- 准确的早期检测对于有效的患者管理至关重要.
- 现有的诊断方法可能耗时,需要专门的专业知识.
研究的目的:
- 开发和验证临床决策支持系统 (CDSS),用于早期检测呼吸系统疾病.
- 使用一维卷积神经网络 (1D-CNN) 来分类肺部声音.
- 提高呼吸系统疾病诊断的准确性和效率.
主要方法:
- 利用ICBHI 2017年呼吸声数据库进行培训和测试.
- 通过重新采样和细分呼吸周期来预处理音频片段.
- 设计并实施了一种1D-CNN模型,其中包含卷积,聚合,脱落和完全连接的层.
- 应用合成少数群体过量采样技术 (SMOTE) 解决阶级不平衡问题.
- 使用网格搜索和k-fold交叉验证优化了超参数.
主要成果:
- 在分类肺部声音方面取得了0.95的整体准确性.
- 在正常 (0.97) 和裂 (0.95) 类别中显示出高F1分数.
- 在肺声分类方面超越现有最先进的方法.
- 通过5倍和10倍的交叉验证验证模型的稳定性.
结论:
- 1D-CNN模型提供了一种准确有效的方法来分类肺部声音.
- 开发的CDSS可以显著帮助医疗从业者诊断呼吸系统疾病.
- 这种人工智能驱动的方法承诺通过更早,更精确的诊断来改善患者护理.
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