Covid 19
COVID-19 research continues to explore the pandemic's multifaceted impact, focusing on accurate prediction of disease severity and mortality, effective diagnosis and treatment strategies, and understanding the spread of misinformation. Current research leverages machine learning, particularly deep learning models like convolutional neural networks and large language models, to analyze diverse data sources including chest X-rays, blood test parameters, and social media posts. These efforts aim to improve clinical decision-making, enhance public health interventions, and ultimately mitigate the long-term consequences of the pandemic.
Papers
A Deep-Learning Framework for Improving COVID-19 CT Image Quality and Diagnostic Accuracy
Garvit Goel, Jingyuan Qi, Wu-chun Feng, Guohua Cao
Quality monitoring of federated Covid-19 lesion segmentation
Camila Gonzalez, Christian Harder, Amin Ranem, Ricarda Fischbach, Isabel Kaltenborn, Armin Dadras, Andreas Bucher, Anirban Mukhopadhyay
Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation
Giscard Biamby, Grace Luo, Trevor Darrell, Anna Rohrbach
Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice
Kranthi Kumar Lella, Alphonse Pja
A literature review on COVID-19 disease diagnosis from respiratory sound data
Kranthi Kumar Lella, Alphonse PJA
COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks
Julianna Antonchuk, Benjamin Prescott, Philip Melanchthon, Robin Singh