Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Fengyu He, Graduate student Ke Feng, Graduate student
Lead Presenter's Name
Ke Feng
Faculty Mentor Name
Ilteris Demirkiran
Abstract
COVID-19 is a large-scale contagious respiratory disease that has spread across the world in 2020. Therefore, a low-cost, fast, and easily available solution is needed to provide a COVID-19 diagnosis to curb the outbreak. According to recent studies, one of the main symptoms of COVID-19 is coughing. The goal of this research effort is to develop a method for the automatic diagnosis of COVID-19 by detecting cough during recorded conversations. The method is composed of five main modules: sound extraction, sound feature extraction, cough detection, cough classification, and COVID-19 diagnosis. The method extracts relevant features from the audio signal and then uses machine learning and deep learning models, like SVM, KNN, and RNN, to classify them, which can successfully diagnose COVID-19 from audio recordings. Our method has relatively high accuracy when dealing with completely unfamiliar cough samples. When the training set and the test set are from two different databases, it still achieves an accuracy of 81.25% (AUC of 0.79). As more data sets are collected, the model can be further developed and improved to create a machine learning solution based on cough analysis for COVID-19 detection, which may be promoted as a non-clinical self-inspection solution.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Spark Grant
Deep-learning Based Approach to Identify Covid-19
COVID-19 is a large-scale contagious respiratory disease that has spread across the world in 2020. Therefore, a low-cost, fast, and easily available solution is needed to provide a COVID-19 diagnosis to curb the outbreak. According to recent studies, one of the main symptoms of COVID-19 is coughing. The goal of this research effort is to develop a method for the automatic diagnosis of COVID-19 by detecting cough during recorded conversations. The method is composed of five main modules: sound extraction, sound feature extraction, cough detection, cough classification, and COVID-19 diagnosis. The method extracts relevant features from the audio signal and then uses machine learning and deep learning models, like SVM, KNN, and RNN, to classify them, which can successfully diagnose COVID-19 from audio recordings. Our method has relatively high accuracy when dealing with completely unfamiliar cough samples. When the training set and the test set are from two different databases, it still achieves an accuracy of 81.25% (AUC of 0.79). As more data sets are collected, the model can be further developed and improved to create a machine learning solution based on cough analysis for COVID-19 detection, which may be promoted as a non-clinical self-inspection solution.