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This paper presents a robust deep learning framework developed to detectrespiratory diseases from recordings of respiratory sounds. The completedetection process firstly involves front end feature extraction whererecordings are transformed into spectrograms that convey both spectral andtemporal information. Then a back-end deep learning model classifies thefeatures into classes of respiratory disease or anomaly. Experiments, conductedover the ICBHI benchmark dataset of respiratory sounds, evaluate the ability ofthe framework to classify sounds. Two main contributions are made in thispaper. Firstly, we provide an extensive analysis of how factors such asrespiratory cycle length, time resolution, and network architecture, affectfinal prediction accuracy. Secondly, a novel deep learning based framework isproposed for detection of respiratory diseases and shown to perform extremelywell compared to state of the art methods.

Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases