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Fedor Fomin, Fahad Panolan, Anurag Patil, Adil Tanveer
Application of machine learning in predicting blood flow and red cell distribution in capillary vessel networks.
In Journal of the Royal Society, Interface
Ebrahimi Saman, Bagchi Prosenjit
blood cell, computational fluid dynamics, haemodynamics, machine learning, microcirculation
Machine learning classification of multiple sclerosis in children using optical coherence tomography.
In Multiple sclerosis (Houndmills, Basingstoke, England)
Ciftci Kavaklioglu Beyza, Erdman Lauren, Goldenberg Anna, Kavaklioglu Can, Alexander Cara, Oppermann Hannah M, Patel Amish, Hossain Soaad, Berenbaum Tara, Yau Olivia, Yea Carmen, Ly Mina, Costello Fiona, Mah Jean K, Reginald Arun, Banwell Brenda, Longoni Giulia, Ann Yeh E
Multiple sclerosis, optical coherence tomography, pediatric, retinal nerve fiber layer thickness, supervised learning
COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images.
In Biocybernetics and biomedical engineering
Fang Lingling, Wang Xin
Adaptive region enhancement, COVID-19, Deep learning, Dense block, Mixed dataset
Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic.
In International journal of applied earth observation and geoinformation : ITC journal
Zhang Yecheng, Zhang Qimin, Zhao Yuxuan, Deng Yunjie, Zheng Hao
Coronavirus disease, Deep learning, Design improvement, Incidence prediction, Spatial risk factors
In Infectious Disease Modelling
Matsunaga Nobuaki, Kamata Keisuke, Asai Yusuke, Tsuzuki Shinya, Sakamoto Yasuaki, Ijichi Shinpei, Akiyama Takayuki, Yu Jiefu, Yamada Gen, Terada Mari, Suzuki Setsuko, Suzuki Kumiko, Saito Sho, Hayakawa Kayoko, Ohmagari Norio
COVID-19, Japan, Machine learning, Risk prediction, Severity