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10.1016/j.bspc.2021.102582 http://scihub22266oqcxt.onion/10.1016/j.bspc.2021.102582 33824680!8015668!33824680     free     free     free Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Signal+Process+Control 2021 ; 68 (ä): 102582 Nephropedia Template TP {{tp|p=33824680|t=2021. Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy |pdf=|usr=}}{{33824680}} gab.com Text Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy JO: Biomed Signal Process Control http://www.kidney.de/pget.php?&tw=1&pmid=33824680 #FOAvip Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( p < 0.01 ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of 89.6% , a sensitivity of 96.1% , and a specificity of 76.9% . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes. Twit Text FOAVip Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy ????Biomed Signal Process Control http://www.kidney.de/pget.php?&tw=1&pmid=33824680 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33824680 #FOAvip English Wikipedia <ref>{{Cite pmid|33824680}}</ref> Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy #MMPMID33824680 Gaudencio AS; Vaz PG; Hilal M; Mahe G; Lederlin M; Humeau-Heurtier A; Cardoso JM Biomed Signal Process Control 2021[Jul]; 68 (ä): 102582 PMID33824680show ga Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( p < 0.01 ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of 89.6% , a sensitivity of 96.1% , and a specificity of 76.9% . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes. äEvaluation of COVID-19 chest computed tomography: A texture analysis b(epmc).pdf DeepDyve Pubget Overpricing