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10.1088/1361-6560/abe838 http://scihub22266oqcxt.onion/10.1088/1361-6560/abe838 33729998!ä!33729998 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Phys+Med+Biol 2021 ; 66 (6): 065031 Nephropedia Template TP {{tp|p=33729998|t=2021. Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification |pdf=|usr=}}{{33729998}} gab.com Text Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification JO: Phys Med Biol http://www.kidney.de/pget.php?&tw=1&pmid=33729998 #FOAvip The worldwide spread of coronavirus disease (COVID-19) has become a threat to global public health. It is of great importance to rapidly and accurately screen and distinguish patients with COVID-19 from those with community-acquired pneumonia (CAP). In this study, a total of 1,658 patients with COVID-19 and 1,027 CAP patients underwent thin-section CT and were enrolled. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to the conventional CT severity score (CT-SS) and radiomics features. An infection size-aware random forest method (iSARF) was proposed for discriminating COVID-19 from CAP. Experimental results show that the proposed method yielded its best performance when using the handcrafted features, with a sensitivity of 90.7%, a specificity of 87.2%, and an accuracy of 89.4% over state-of-the-art classifiers. Additional tests on 734 subjects, with thick slice images, demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making. Twit Text FOAVip Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification ????Phys Med Biol http://www.kidney.de/pget.php?&tw=1&pmid=33729998 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33729998 #FOAvip English Wikipedia <ref>{{Cite pmid|33729998}}</ref> Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification #MMPMID33729998 Shi F; Xia L; Shan F; Song B; Wu D; Wei Y; Yuan H; Jiang H; He Y; Gao Y; Sui H; Shen D Phys Med Biol 2021[Mar]; 66 (6): 065031 PMID33729998show ga The worldwide spread of coronavirus disease (COVID-19) has become a threat to global public health. It is of great importance to rapidly and accurately screen and distinguish patients with COVID-19 from those with community-acquired pneumonia (CAP). In this study, a total of 1,658 patients with COVID-19 and 1,027 CAP patients underwent thin-section CT and were enrolled. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to the conventional CT severity score (CT-SS) and radiomics features. An infection size-aware random forest method (iSARF) was proposed for discriminating COVID-19 from CAP. Experimental results show that the proposed method yielded its best performance when using the handcrafted features, with a sensitivity of 90.7%, a specificity of 87.2%, and an accuracy of 89.4% over state-of-the-art classifiers. Additional tests on 734 subjects, with thick slice images, demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making. |*Tomography, X-Ray Computed[MESH] |Adult[MESH] |Aged[MESH] |COVID-19/*diagnostic imaging[MESH] |Community-Acquired Infections/*diagnostic imaging[MESH] |Diagnosis, Computer-Assisted[MESH] |Diagnosis, Differential[MESH] |Female[MESH] |Humans[MESH] |Image Processing, Computer-Assisted[MESH] |Lung/diagnostic imaging/virology[MESH] |Male[MESH] |Middle Aged[MESH] |Pneumonia/*diagnostic imaging[MESH] |Reproducibility of Results[MESH] |Retrospective Studies[MESH]Large-scale screening to distinguish between COVID-19 and community-ac(epmc).pdf DeepDyve Pubget Overpricing