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10.1038/s42003-020-01535-7 http://scihub22266oqcxt.onion/10.1038/s42003-020-01535-7 33398067!7782580!33398067     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       Commun+Biol 2021 ; 4 (1): 35 Nephropedia Template TP {{tp|p=33398067|t=2021. Fast automated detection of COVID-19 from medical images using convolutional neural networks |pdf=|usr=}}{{33398067}} gab.com Text Fast automated detection of COVID-19 from medical images using convolutional neural networks JO: Commun Biol http://www.kidney.de/pget.php?&tw=1&pmid=33398067 #FOAvip Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice. Twit Text FOAVip Fast automated detection of COVID-19 from medical images using convolutional neural networks ????Commun Biol http://www.kidney.de/pget.php?&tw=1&pmid=33398067 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33398067 #FOAvip English Wikipedia <ref>{{Cite pmid|33398067}}</ref> Fast automated detection of COVID-19 from medical images using convolutional neural networks #MMPMID33398067 Liang S; Liu H; Gu Y; Guo X; Li H; Li L; Wu Z; Liu M; Tao L Commun Biol 2021[Jan]; 4 (1): 35 PMID33398067show ga Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice. |*Deep Learning[MESH] |*Neural Networks, Computer[MESH] |Algorithms[MESH] |COVID-19/*diagnosis/epidemiology/virology[MESH] |Humans[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH] |Reverse Transcriptase Polymerase Chain Reaction[MESH] |SARS-CoV-2/genetics/*isolation & purification/physiology[MESH] |Sensitivity and Specificity[MESH]Fast automated detection of COVID-19 from medical images using convolu(epmc).pdf DeepDyve Pubget Overpricing