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.

中文翻译：

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基于先验的网络和多谱图集合用于预测呼吸异常和肺部疾病

本文提出了一种基于先验的深度神经网络，用于通过呼吸声输入检测肺部疾病。首先将从患者那里收集到的呼吸声记录转换成频谱图，在频谱图中可以很好地呈现频谱和时间信息，称为前端特征提取。然后，将这些频谱图输入到提议的网络中，称为后端分类，以检测患者是否患有与肺相关的疾病。我们在呼吸音的ICBHI基准元数据集上进行的实验在呼吸异常和疾病检测方面的竞争性ICBHI得分分别为0.53 / 0.45和0.87 / 0.85。