Machine learning models for COVID-19 diagnosis based on medical images and audio

Abstract

The COVID-19 pandemic presents a significant danger to human health, with far-reaching consequences for the global economy and political dynamics. It presents complex challenges in terms of rapid and accurate diagnosis, where machine learning holds potential to enhance diagnostic speed and precision while reducing time and resource burdens. Consequently, this research employs CT images and cough audio recordings as training data to create machine learning models for data classification, with the goal of aiding in the diagnosis of COVID-19. Using the Edge Impulse platform, a Convolutional Neural Network, MobileNetV2, is customized for efficient image recognition. On the audio front, the preprocessing phase encompassed three distinct feature extraction techniques: Mel Frequency Cepstral Coefficients, Mel-Filterbank Energy (MFE), and Mel spectrogram. Subsequently, model frameworks were meticulously adjusted to accommodate the classification requirements. The results of this effort are highly encouraging. In the domain of CT image recognition, the top-performing model achieved a remarkable accuracy of 93.98%. In the concurrent task of categorizing cough audio data, the best performance reached 81.8%. These findings underscore the capability of this approach as an effective supplementary tool for medical diagnostics. In the face of COVID-19s persisting impact, such machine learning advancements could significantly aid in swift and reliable diagnoses, ultimately contributing to the global battle against the pandemic.