Multi Feature fusion for COPD Classification using Deep learning algorithms

Abstract

Machine learning (ML) and deep learning (DL) are becoming pivotal for providing solutions to healthcare issues. Due to their accurate and quick forecasting models and discoveries, ML and DL algorithms are being used for disease classification by healthcare experts. Along with life-threatening illnesses like cancer, respiratory problems such as Chronic Obstructive Pulmonary Disease (COPD) have been growing more prevalent and endangering the survival of human society. According to the World Health Organization, COPD will be the third-leading cause of death and the seventh-leading cause of illness globally by 2030. Therefore, early detection and fast treatment are essential. The primary methods for diagnosing COPD need inadequate and pricy spirometer and imaging equipment. In this paper, an attempt is made to determine the severity of COPD disease using ML and DL algorithms using the cough sound of the patient. To extract audio features like Mfcc, Chroma, Contract, Mel, and Tonnetz, we have used the Librosa Python Library. To address the issues of imbalanced dataset, we have used the SMOTE algorithm. To find the most effective multi feature fusion for classifying COPD, numerous experiments have been carried out using various fusions of audio features. For the purpose of evaluating the multifeature fusion's performance, we have run MLP, CNN, RNN, and LSTM models on fusion of two audio features and three audio features. Results of experiments suggest that the LSTM model with Adam as an optimization function gives 100% training accuracy and 87% testing accuracy for fusion of Mfcc and Mel features. As a result of the fusion of the three features of Tonnetz, Chroma, and Mel, CNN model performs better with training accuracy of 90% and testing accuracy of 82%.