基于人工智能的框架,从使用深度CNN的数字耳语仪数据中识别COVID-19疾病和其他常见呼吸道疾病中的异常
Kranthi Kumar Lella1, M S Jagadeesh1, P J A Alphonse2
1School of Computer Science and Engineering, VIT-AP University, Vijayawada, Guntur, Andhra Pradesh 522237 India.
Health information science and systems
|March 12, 2024
概括
这项研究引入了一个深度卷积神经网络 (DCNN) 模型,使用数字听力镜数据的多功能通道来诊断肺部疾病. 使用最大共享的DCNN在识别呼吸系统疾病症状方面表现出卓越的表现.
科学领域:
- 医疗声学 医学声学
- 医疗保健中的人工智能
- 用于诊断的信号处理.
背景情况:
- 使用数字耳语镜进行呼吸道声音分析对于诊断肺部疾病越来越重要.
- 人工智能 (AI) 提供了有希望的方法来区分呼吸系统疾病指标与肺部听觉声音.
- 正在探索深度学习模型来解释复杂的呼吸声特征.
研究的目的:
- 开发和评估一个深度卷积神经网络 (DCNN) 模型,用于使用数字耳语镜数据诊断呼吸系统疾病.
- 调查多功能频道 (修改MFCC,Log Mel,Soft Mel) 和最大集成对DCNN性能的影响.
- 通过结合COVID-19声音数据和L2规范化来提高模型的稳定性,以提高概括性.
主要方法:
- 实现一个DCNN模型,集成来自数字听力镜录音的修改MFCC,Log Mel和Soft Mel特征.
- 对DCNN性能进行比较分析,包括和不包括最大集结操作.
- 在丰富的数据集上训练和测试模型,包括COVID-19声音数据和应用L2规范化以减轻过度拟合.
- 用各种卷积过器大小 (例如,3x3,5x5,7x7) 进行实验,以优化神经网络架构.
主要成果:
- 与没有最大共享的模型相比,使用最大共享的DCNN模型在识别呼吸系统疾病症状方面表现优越.
- 多特征通道光谱图方法,结合最大化聚合,在增强数据集上取得了最先进的结果.
- 包括COVID-19声音数据和L2规范化有效地解决了过度装配问题,提高了整体模型性能.
结论:
- 拟议的DCNN架构,特别是具有最大共享和多功能通道的架构,对于从数字耳语镜数据中诊断呼吸系统疾病非常有效.
- 最大聚合显著提高了DCNN提取相关特征以准确识别症状的能力.
- 该研究强调了人工智能驱动的呼吸声分析对改善临床诊断的潜力.
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