Real-Time Cough Detection and Classification of COVID-19 Using LSTM-Based Sound Separation and Lightweight CNN Models

Abstract

COVID-19, a respiratory disease, caused severe human, social, and economic loss worldwide. Early-stage diagnosis of COVID-19 can help to mitigate its spread and health complications. However, existing diagnosis methods involve high costs and can put healthcare professionals at risk of infection. To address these challenges, this paper presents a lightweight sound separation based on Long Short-Term Memory (LSTM) and lightweight Convolutional Neural Network ( CNN) model    for real-time detection and classification of COVID19 based on cough sounds. The proposed approach does not require the in-person presence of patients, eliminating the risk of spreading the virus. Background noises in cough sounds pose a significant challenge to classification accuracy. This study acquires cough sound data from six credible sources, removes background noises from them using a deep learning technique, and finally includes 1,886 COVID-19-positive and 1,757 COVID-19-negative samples in the dataset. The performance of deep learning models i.e., MobileNetV2, MobileNetV3 Small, and EfficientNet-lite-0 is evaluated using the confusion matrix. Results indicate that MobileNetV3 Small outperforms all other models with an accuracy of 99%, making it the best choice for real-time detection and classification of cough-based COVID-19.