论文标题
基于Inception的网络和多光谱图集合用于预测呼吸异常和肺部疾病
Inception-Based Network and Multi-Spectrogram Ensemble Applied For Predicting Respiratory Anomalies and Lung Diseases
论文作者
论文摘要
本文提出了一种基于呼吸道音量输入的基于Inception的深神网络，用于检测肺部疾病。首先将从患者收集的呼吸道声记录转换为光谱图和时间信息的光谱图，称为前端特征提取。然后将这些频谱图馈送到所提出的网络中，称为后端分类，用于检测患者是否患有与肺部相关疾病。我们的实验在呼吸道声的洲际基准元基准元数据上进行，分别在呼吸异常和疾病检测方面，竞争性洲际际之流分别为0.53/0.45和0.87/0.85。
This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are firstly transformed into spectrograms where both spectral and temporal information are well presented, referred to as front-end feature extraction. These spectrograms are then fed into the proposed network, referred to as back-end classification, for detecting whether patients suffer from lung-relevant diseases. Our experiments, conducted over the ICBHI benchmark meta-dataset of respiratory sound, achieve competitive ICBHI scores of 0.53/0.45 and 0.87/0.85 regarding respiratory anomaly and disease detection, respectively.
