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
Two-Stage Classifier for COVID-19 Misinformation Detection Using BERT: a Study on Indonesian Tweets
Douglas Raevan Faisal, Rahmad Mahendra
COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings
Daniel Kienzle, Julian Lorenz, Robin Schön, Katja Ludwig, Rainer Lienhart
PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis
Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan
AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging
Dimitrios Kollias, Anastasios Arsenos, Stefanos Kollias
Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM
Hamid Nasiri, Ghazal Kheyroddin, Morteza Dorrigiv, Mona Esmaeili, Amir Raeisi Nafchi, Mohsen Haji Ghorbani, Payman Zarkesh-Ha
Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms
Debarpan Bhattacharya, Debottam Dutta, Neeraj Kumar Sharma, Srikanth Raj Chetupalli, Pravin Mote, Sriram Ganapathy, Chandrakiran C, Sahiti Nori, Suhail K K, Sadhana Gonuguntla, Murali Alagesan
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Images
Hayden Gunraj, Tia Tuinstra, Alexander Wong
Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images
Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama