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10.1007/s00521-020-05641-9 http://scihub22266oqcxt.onion/10.1007/s00521-020-05641-9 33649695!7905772!33649695     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Neural+Comput+Appl 2021 ; ä (ä): 1-16 Nephropedia Template TP {{tp|p=33649695|t=2021. Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks |pdf=|usr=}}{{33649695}} gab.com Text Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks JO: Neural Comput Appl http://www.kidney.de/pget.php?&tw=1&pmid=33649695 #FOAvip The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis, but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well-known pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison with the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity. Twit Text FOAVip Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks ????Neural Comput Appl http://www.kidney.de/pget.php?&tw=1&pmid=33649695 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33649695 #FOAvip English Wikipedia <ref>{{Cite pmid|33649695}}</ref> Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks #MMPMID33649695 Dev K; Khowaja SA; Bist AS; Saini V; Bhatia S Neural Comput Appl 2021[Feb]; ä (ä): 1-16 PMID33649695show ga The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis, but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well-known pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison with the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity. äTriage of potential COVID-19 patients from chest X-ray images using hi(epmc).pdf DeepDyve Pubget Overpricing