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10.1016/j.inffus.2020.10.004 http://scihub22266oqcxt.onion/10.1016/j.inffus.2020.10.004 33052196!7544601!33052196     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Inf+Fusion 2021 ; 67 (ä): 208-229 Nephropedia Template TP {{tp|p=33052196|t=2021. Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network |pdf=|usr=}}{{33052196}} gab.com Text Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network JO: Inf Fusion http://www.kidney.de/pget.php?&tw=1&pmid=33052196 #FOAvip (Aim) COVID-19 is an infectious disease spreading to the world this year. In this study, we plan to develop an artificial intelligence based tool to diagnose on chest CT images. (Method) On one hand, we extract features from a self-created convolutional neural network (CNN) to learn individual image-level representations. The proposed CNN employed several new techniques such as rank-based average pooling and multiple-way data augmentation. On the other hand, relation-aware representations were learnt from graph convolutional network (GCN). Deep feature fusion (DFF) was developed in this work to fuse individual image-level features and relation-aware features from both GCN and CNN, respectively. The best model was named as FGCNet. (Results) The experiment first chose the best model from eight proposed network models, and then compared it with 15 state-of-the-art approaches. (Conclusion) The proposed FGCNet model is effective and gives better performance than all 15 state-of-the-art methods. Thus, our proposed FGCNet model can assist radiologists to rapidly detect COVID-19 from chest CT images. Twit Text FOAVip Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network ????Inf Fusion http://www.kidney.de/pget.php?&tw=1&pmid=33052196 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33052196 #FOAvip English Wikipedia <ref>{{Cite pmid|33052196}}</ref> Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network #MMPMID33052196 Wang SH; Govindaraj VV; Gorriz JM; Zhang X; Zhang YD Inf Fusion 2021[Mar]; 67 (ä): 208-229 PMID33052196show ga (Aim) COVID-19 is an infectious disease spreading to the world this year. In this study, we plan to develop an artificial intelligence based tool to diagnose on chest CT images. (Method) On one hand, we extract features from a self-created convolutional neural network (CNN) to learn individual image-level representations. The proposed CNN employed several new techniques such as rank-based average pooling and multiple-way data augmentation. On the other hand, relation-aware representations were learnt from graph convolutional network (GCN). Deep feature fusion (DFF) was developed in this work to fuse individual image-level features and relation-aware features from both GCN and CNN, respectively. The best model was named as FGCNet. (Results) The experiment first chose the best model from eight proposed network models, and then compared it with 15 state-of-the-art approaches. (Conclusion) The proposed FGCNet model is effective and gives better performance than all 15 state-of-the-art methods. Thus, our proposed FGCNet model can assist radiologists to rapidly detect COVID-19 from chest CT images. äCovid-19 classification by FGCNet with deep feature fusion from graph (epmc).pdf DeepDyve Pubget Overpricing