Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks

Abstract

IntroductionIn order to improve the diagnostic accuracy of respiratory illnesses, our research introduces a novel methodology to precisely diagnose a subset of lung diseases using patient respiratory audio recordings. These lung diseases include Chronic Obstructive Pulmonary Disease (COPD), Upper Respiratory Tract Infections (URTI), Bronchiectasis, Pneumonia, and Bronchiolitis.MethodsOur proposed methodology trains four deep learning algorithms on an input dataset consisting of 920 patient respiratory audio files. These audio files were recorded using digital stethoscopes and comprise the Respiratory Sound Database. The four deployed models are Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN ensembled with unidirectional LSTM (CNN-LSTM), and CNN ensembled with bidirectional LSTM (CNN-BLSTM).ResultsThe aforementioned models are evaluated using metrics such as accuracy, precision, recall, and F1-score. The best performing algorithm, LSTM, has an overall accuracy of 98.82% and F1-score of 0.97.DiscussionThe LSTM algorithm's extremely high predictive accuracy can be attributed to its penchant for capturing sequential patterns in time series based audio data. In summary, this algorithm is able to ingest patient audio recordings and make precise lung disease predictions in real-time.

Efficient Deepfake Audio Detection Using Spectro-Temporal Analysis and Deep Learning

“…The "Enhanced Deepfake Audio Detection with Spectro-Temporal Deep Learning Approach" builds upon this foundation by integrating advanced deep learning techniques with a keen analysis of Spectro-temporal features. The approach harnesses the influence of convolutional neural networks (CNNs) to take out detailed spectral features and recurrent neural networks (RNNs) or long short-term memory (LSTM) networks to confine temporal dependency in audio signals [2]. This dual-focus model is adept at identifying subtle anomalies and inconsistencies that distinguish deepfake audio from genuine recordings, significantly improving detection accuracy.…”