https://socictopen.socict.org/files/original/a52810f0c90311196a433c0d0ae58e42.pdf a2c0b1630ecbe42cd9327336beebe94e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource DenseCapsNet: Detection of COVID-19 from X-ray images using a capsule neural network. Creator An entity primarily responsible for making the resource Hao Quan, Xiaosong Xu, Tingting Zheng, Zhi Li, Mingfang Zhao, Xiaoyu Cui Description An account of the resource At present, the global pandemic as it relates to novel coronavirus pneumonia is still a very difficult situation. Due to the recent outbreak of novel coronavirus pneumonia, novel chest X-ray (CXR) images that can be used for deep learning analysis are very rare. To solve this problem, we propose a deep learning framework that integrates a convolutional neural network and a capsule network. DenseCapsNet, a new deep learning framework, is formed by the fusion of a dense convolutional network (DenseNet) and the capsule neural network (CapsNet), leveraging their respective advantages and reducing the dependence of convolutional neural networks on a large amount of data. Using 750 CXR images of lungs of healthy patients as well as those of patients with other pneumonia and novel coronavirus pneumonia, the method can obtain an accuracy of 90.7% and an F1 score of 90.9%, and the sensitivity for detecting COVID-19 can reach 96%. These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia CXR radiography detection. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource covid-19, deep learning, chest x-ray, Classification, Capsule neural network Identifier An unambiguous reference to the resource within a given context 10.1016/j.compbiomed.2021.104399 Source A related resource from which the described resource is derived Computers in biology and medicine