Covid-19 Classification Using Cough Audio - A Comparison Of Deep Learning Vs Traditional Ml Algorithms

The COVID-19 pandemic, declared by the World Health Organization in March 2020, originated in Wuhan, China and quickly spread worldwide. In response to the global health crisis, scientific cooperation across the globe has intensified, with a critical focus on leveraging machine learning and deep learning for faster and non-invasive medical diagnostics. This study aims to contribute to these efforts by utilizing cough audio signals to detect COVID-19. It proposes a comprehensive study that starts with gathering and preprocessing cough audio data. The research further enhances the performance and generalization of deep learning models through transfer learning, employing VGG19 to adapt pretrained neural networks for this specific task. Alongside evaluating the models’ performance, the study also explores their interpretability and explainability, which are crucial for practical implementation. The outcome of this research is expected to provide a reliable, non-invasive, cost-effective, and scalable method for early detection of COVID-19, potentially easing the heavy reliance on traditional RT-PCR testing. Through the comparative analysis of machine learning and deep learning models in this context, the study also aims to provide deeper insights into the effectiveness of these computational approaches in tackling global health challenges.