  Respiratory diseases are among the most common causes of severe illness and
death worldwide. Prevention and early diagnosis are essential to limit or even
reverse the trend that characterizes the diffusion of such diseases. In this
regard, the development of advanced computational tools for the analysis of
respiratory auscultation sounds can become a game changer for detecting
disease-related anomalies, or diseases themselves. In this work, we propose a
novel learning framework for respiratory auscultation sound data. Our approach
combines state-of-the-art feature extraction techniques and advanced
deep-neural-network architectures. Remarkably, to the best of our knowledge, we
are the first to model a recurrent-neural-network based learning framework to
support the clinician in detecting respiratory diseases, at either level of
abnormal sounds or pathology classes. Results obtained on the ICBHI benchmark
dataset show that our approach outperforms competing methods on both
anomaly-driven and pathology-driven prediction tasks, thus advancing the
state-of-the-art in respiratory disease analysis.
