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10.3389/fmed.2020.612962 http://scihub22266oqcxt.onion/10.3389/fmed.2020.612962 33585511!7875085!33585511     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Med+(Lausanne) 2020 ; 7 (ä): 612962 Nephropedia Template TP {{tp|p=33585511|t=2020. Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network |pdf=|usr=}}{{33585511}} gab.com Text Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network JO: Front Med (Lausanne) http://www.kidney.de/pget.php?&tw=1&pmid=33585511 #FOAvip A three-dimensional (3D) deep learning method is proposed, which enables the rapid diagnosis of coronavirus disease 2019 (COVID-19) and thus significantly reduces the burden on radiologists and physicians. Inspired by the fact that the current chest computed tomography (CT) datasets are diversified in equipment types, we propose a COVID-19 graph in a graph convolutional network (GCN) to incorporate multiple datasets that differentiate the COVID-19 infected cases from normal controls. Specifically, we first apply a 3D convolutional neural network (3D-CNN) to extract image features from the initial 3D-CT images. In this part, a transfer learning method is proposed to improve the performance, which uses the task of predicting equipment type to initialize the parameters of the 3D-CNN structure. Second, we design a COVID-19 graph in GCN based on the extracted features. The graph divides all samples into several clusters, and samples with the same equipment type compose a cluster. Then we establish edge connections between samples in the same cluster. To compute accurate edge weights, we propose to combine the correlation distance of the extracted features and the score differences of subjects from the 3D-CNN structure. Lastly, by inputting the COVID-19 graph into GCN, we obtain the final diagnosis results. In experiments, the dataset contains 399 COVID-19 infected cases, and 400 normal controls from six equipment types. Experimental results show that the accuracy, sensitivity, and specificity of our method reach 98.5%, 99.9%, and 97%, respectively. Twit Text FOAVip Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network ????Front Med (Lausanne) http://www.kidney.de/pget.php?&tw=1&pmid=33585511 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33585511 #FOAvip English Wikipedia <ref>{{Cite pmid|33585511}}</ref> Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network #MMPMID33585511 Liang X; Zhang Y; Wang J; Ye Q; Liu Y; Tong J Front Med (Lausanne) 2020[]; 7 (ä): 612962 PMID33585511show ga A three-dimensional (3D) deep learning method is proposed, which enables the rapid diagnosis of coronavirus disease 2019 (COVID-19) and thus significantly reduces the burden on radiologists and physicians. Inspired by the fact that the current chest computed tomography (CT) datasets are diversified in equipment types, we propose a COVID-19 graph in a graph convolutional network (GCN) to incorporate multiple datasets that differentiate the COVID-19 infected cases from normal controls. Specifically, we first apply a 3D convolutional neural network (3D-CNN) to extract image features from the initial 3D-CT images. In this part, a transfer learning method is proposed to improve the performance, which uses the task of predicting equipment type to initialize the parameters of the 3D-CNN structure. Second, we design a COVID-19 graph in GCN based on the extracted features. The graph divides all samples into several clusters, and samples with the same equipment type compose a cluster. Then we establish edge connections between samples in the same cluster. To compute accurate edge weights, we propose to combine the correlation distance of the extracted features and the score differences of subjects from the 3D-CNN structure. Lastly, by inputting the COVID-19 graph into GCN, we obtain the final diagnosis results. In experiments, the dataset contains 399 COVID-19 infected cases, and 400 normal controls from six equipment types. Experimental results show that the accuracy, sensitivity, and specificity of our method reach 98.5%, 99.9%, and 97%, respectively. äDiagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network (epmc).pdf DeepDyve Pubget Overpricing