Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1109/TCBB.2021.3065361 http://scihub22266oqcxt.onion/10.1109/TCBB.2021.3065361 33705321!8851430!33705321     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE/ACM+Trans+Comput+Biol+Bioinform 2021 ; 18 (6): 2775-2780 Nephropedia Template TP {{tp|p=33705321|t=2021. Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images |pdf=|usr=}}{{33705321}} gab.com Text Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images JO: IEEE/ACM Trans Comput Biol Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33705321 #FOAvip A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn/model.php). Source codes and datasets are available at our GitHub (https://github.com/SY575/COVID19-CT). Twit Text FOAVip Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images ????IEEE/ACM Trans Comput Biol Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33705321 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33705321 #FOAvip English Wikipedia <ref>{{Cite pmid|33705321}}</ref> Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images #MMPMID33705321 Song Y; Zheng S; Li L; Zhang X; Zhang X; Huang Z; Chen J; Wang R; Zhao H; Chong Y; Shen J; Zha Y; Yang Y IEEE/ACM Trans Comput Biol Bioinform 2021[Nov]; 18 (6): 2775-2780 PMID33705321show ga A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn/model.php). Source codes and datasets are available at our GitHub (https://github.com/SY575/COVID19-CT). |*Deep Learning[MESH] |COVID-19/*diagnosis/*diagnostic imaging[MESH] |Case-Control Studies[MESH] |China[MESH] |Computational Biology[MESH] |Diagnosis, Computer-Assisted/*statistics & numerical data[MESH] |Diagnosis, Differential[MESH] |Humans[MESH] |Models, Statistical[MESH] |Pneumonia, Bacterial/diagnosis/diagnostic imaging[MESH] |SARS-CoV-2[MESH]Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-1(epmc).pdf DeepDyve Pubget Overpricing