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.
Papers
UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio
Jiangeng Chang, Yucheng Ruan, Cui Shaoze, John Soong Tshon Yit, Mengling Feng
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case
Febrianti Wibawa, Ferhat Ozgur Catak, Salih Sarp, Murat Kuzlu, Umit Cali