  This paper presents a robust deep learning framework developed to detect
respiratory diseases from recordings of respiratory sounds. The complete
detection process firstly involves front end feature extraction where
recordings are transformed into spectrograms that convey both spectral and
temporal information. Then a back-end deep learning model classifies the
features into classes of respiratory disease or anomaly. Experiments, conducted
over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of
the framework to classify sounds. Two main contributions are made in this
paper. Firstly, we provide an extensive analysis of how factors such as
respiratory cycle length, time resolution, and network architecture, affect
final prediction accuracy. Secondly, a novel deep learning based framework is
proposed for detection of respiratory diseases and shown to perform extremely
well compared to state of the art methods.
