REAL-TIME RESPIRATORY SOUND CLASSIFICATION FOR REMOTE DIAGNOSTIC SYSTEMS UTILIZING DEEP LEARNING AND SPECTRUM ANALYSIS

The detection of abnormal lung sounds is a critical issue that falls under the diagnosis of respiratory conditions and holds promising developments in deep learning to address such issues. This study proposes a new methodology for classifying adventitious RS by using a remote stethoscope vest coat installed with deep CNNs. The process begins with preprocessing the raw audio data into standard waveforms, transforming the latter waveforms to spectrograms for further processing. Another Fourier Transform is carried out on the data to extract its frequency features that aid in improving discriminative patterns identification in lung sounds. Horizontal flipping is among the techniques for augmenting the data to avoid overfitting. Classifiers such as VGG, AlexNet, ResNet, Inception Net, and LeNet were tested for their classification performance in respiratory sound spectrograms. From all the models built with VGG, VGG-B1 proved to have the highest precision, recall, and accuracy values (96%). There are four types of aberrant lung sounds in the dataset: wheeze, rhonchi, stridor, and crackles, which are obtained from R.A.L.E. Lung Sounds and Easy Auscultation. The proposed system, therefore, provides an efficient and robust solution in real-time detection and classification of abnormal lung sounds as a step toward remote monitoring for early diagnosis of respiratory disorders.