COVID 19 Detection

COVID-19 detection research focuses on developing accurate and robust diagnostic tools using diverse data sources, including chest X-rays, CT scans, blood tests, and even cough audio. Current research heavily utilizes deep learning models, such as convolutional neural networks (CNNs), vision transformers (ViTs), and recurrent neural networks (RNNs), often incorporating techniques like transfer learning, domain adaptation, and ensemble methods to improve performance and address data limitations. These advancements aim to improve the speed, accuracy, and accessibility of COVID-19 diagnosis, ultimately impacting public health management and clinical workflows.