Lung Sound Classification With Multi-Feature Integration Utilizing Lightweight CNN Model

Abstract

Detecting respiratory diseases is of utmost importance, considering that respiratory ailments represent one of the most prevalent categories of diseases globally. The initial stage of lung disease detection involves auscultation conducted by specialists, relying significantly on their expertise. Therefore, automating the auscultation process for the detection of lung diseases can yield enhanced efficiency. Artificial intelligence (AI) has shown promise in improving the accuracy of lung sound classification by extracting features from lung sounds that are relevant to the classification task and learning the relationships between these features and the different pulmonary diseases. This paper utilizes two publicly available respiratory sound recordings namely, ICBHI 2017 challenge dataset and another lung sound dataset available at Mendeley Data. Foremost in this paper, we provide a detailed exposition about employing a Convolutional Neural Network (CNN) that utilizes feature extraction from Mel spectrograms, Mel frequency cepstral coefficients (MFCCs), and Chromagram. The highest accuracy achieved in the developed classification is 91.04% for 10 classes. Extending the contribution, this paper elaborates on the explanation of the classification model prediction by employing Explainable Artificial Intelligence (XAI). The novel contribution of this study is a CNN model that classifies lung sounds into 10 classes by combining audio-specific features to enhance the classification process.

Keywords

• Artificial intelligence
• explainability
• respiratory diseases
• sound processing