High-accuracy lung sound classification for healthy versus unhealthy diagnosis using artificial neural network.

INTRODUCTION: In recent years, advancements in machine learning and electronic stethoscope technology have enabled high-precision recording and analysis of lung sounds, significantly enhancing pulmonary disease diagnosis. METHODS: This study presents a comprehensive approach to classify lung sounds into healthy and unhealthy categories using a dataset collected from 112 subjects, comprising 35 healthy individuals and 77 patients with various pulmonary conditions, such as asthma, heart failure, pneumonia, bronchitis, pleural effusion, lung fibrosis, and chronic obstructive pulmonary disease (COPD), grouped as unhealthy. The dataset was obtained using a 3M Littmann® Electronic Stethoscope Model 3,200, employing three types of filters (Bell, Diaphragm, and Extended) to capture sounds across different frequency ranges. We extracted five key audio features-Spectral Centroid, Power, Energy, Zero Crossing Rate, and Mel-Frequency Cepstral Coefficients (MFCCs)-from each recording to form a feature matrix. A Multi-Layer Perceptron (MLP) neural network was trained for binary classification. RESULTS: The MLP neural network achieved accuracies of 98%, 100%, and 94% on the training, validation, and testing sets, respectively. This partitioning ensured the model's robustness and accuracy. DISCUSSION: The high classification accuracy achieved by the MLP neural network suggests that this approach is a valuable decision-support tool for identifying healthy versus unhealthy lung sounds in clinical settings, facilitating early intervention while maintaining computational efficiency for offline implementation. The combination of detailed feature extraction and an optimized MLP neural network resulted in a reliable method for automated binary classification of lung sounds.