计算机与数字工程2025，Vol.53Issue(3)：678-683,6.DOI:10.3969/j.issn.1672-9722.2025.03.012
多输入神经网络的肺气肿识别
Emphysema Recognition by Multi-input Neural Network
摘要
Abstract
Multiple input neural network is used to classify the two typical characteristics of emphysema(stridor and blister),so as to determine whether high altitude emphysema is present or not.For lung sound data,four spectral feature extraction methods including Mel spectrum(Mel),constant Q transform(CQT),Wavelet transform(WT)and short-time Fourier transform(STFT)are used after filtering and denoising.LBP and Mixup are used for data enhancement,and lung sound classification is performed in Mul-CNN.The accuracy,specificity,sensitivity and ICBHI scores of lung sounds are 93.6%,92.3%,94%and 93.1%when WT and Mel are used as inputs.关键词
肺气肿/Mul-CNN/LBP/MixupKey words
emphysema/Mul-CNN/LBP/Mixup分类
信息技术与安全科学引用本文复制引用
郭涛,古依聪,刘启明,李成,石帅..多输入神经网络的肺气肿识别[J].计算机与数字工程,2025,53(3):678-683,6.基金项目
国家自然科学基金项目(编号:51975541)资助. （编号:51975541）
