IoMT-Enabled Noninvasive Lungs Disease Detection and Classification Using Deep Learning-Based Analysis of Lungs Sounds

Noninvasive and accurate methods for diagnosing respiratory diseases are essential to improving healthcare consequences. The Internet of Medical Things (IoMT) is critical in driving developments in this field. This work presents an IoMT-enabled approach for lung disease detection and classification, using deep learning techniques to analyze lung sounds. The proposed approach uses three datasets: the Respiratory Sound, the Coronahack Respiratory Sound, and the Coswara Sound. Traditional machine learning models, including the Extra Tree Classifier and AdaBoost Classifier, are used to benchmark performance. The Extra Tree Classifier achieved 94.12%, 95.23%, and 94.21% across the datasets, while the AdaBoost Classifier showed improvements with 95.42%, 96.33%, and 94.76%. The proposed deep neural network (DNN) achieves accuracies of 98.92%, 99.33%, and 99.36% for the same datasets. This study explores the transformative potential of the Internet of Medical Things (IoMT) in augmenting diagnostic precision and advancing the field of respiratory healthcare.