Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases
This work addresses respiratory disease detection for medical applications, but it appears incremental as it builds on existing deep learning methods with specific optimizations.
The paper tackles the problem of detecting respiratory diseases from sound recordings by developing a deep learning framework that extracts spectrogram features and classifies them, achieving high performance on the ICBHI benchmark dataset.
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.