Coronavirus diagnosis using cough sounds: Artificial intelligence approaches

Abstract

IntroductionThe Coronavirus disease 2019 (COVID-19) pandemic has caused irreparable damage to the world. In order to prevent the spread of pathogenicity, it is necessary to identify infected people for quarantine and treatment. The use of artificial intelligence and data mining approaches can lead to prevention and reduction of treatment costs. The purpose of this study is to create data mining models in order to diagnose people with the disease of COVID-19 through the sound of coughing.MethodIn this research, Supervised Learning classification algorithms have been used, which include Support Vector Machine (SVM), random forest, and Artificial Neural Networks, that based on the standard “Fully Connected” neural network, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) recurrent neural networks have been established. The data used in this research was from the online site sorfeh.com/sendcough/en, which has data collected during the spread of COVID-19.ResultWith the data we have collected (about 40,000 people) in different networks, we have reached acceptable accuracies.ConclusionThese findings show the reliability of this method for using and developing a tool as a screening and early diagnosis of people with COVID-19. This method can also be used with simple artificial intelligence networks so that acceptable results can be expected. Based on the findings, the average accuracy was 83% and the best model was 95%.

Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

“…Supervised Learning classification algorithms, such as Support Vector Machine, random forest, Artificial Neural Networks, Fully Connected neural network, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) recurrent neural networks were employed. The models achieved an average accuracy of 83%, with LSTM reaching the highest accuracy at 95% (Askari Nasab et al, 2023 ).…”

Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics

“…In the fight against COVID-19-induced pneumonia, another important application is cough detection. Recent studies reveal that audio sounds produced by the respiratory system, such as coughing, can be analyzed to determine whether a patient is infected with COVID-19 [ 7 ]. This is a significant breakthrough as it allows for non-invasive and rapid screening of potential COVID-19 cases.…”

“…This highlights the potential of using advanced signal processing techniques and machine learning models in COVID-19 detection. Other works can be found in several review articles about this topic [ 7 , 12 ]. These articles provide comprehensive overviews of the current state-of-the-art in COVID-19 detection using cough sounds.…”