# Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases Source: [Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:05:19.802Z Authors: Lam Pham, Ian McLoughlin, Huy Phan, Minh Tran, Truc Nguyen, Ramaswamy Palaniappan Published: 2020-01-21T15:26:52.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Ternary Chronic Classification on ICBHI 2017 (train-val)](): Ternary Chronic Classification; ICBHI 2017 (train-val); Specificity: 86. ## Original sources and implementation [Original paper]() [PDF]()