  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.
