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10.7717/peerj-cs.345 http://scihub22266oqcxt.onion/10.7717/peerj-cs.345 33834092!8022578!33834092     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       PeerJ+Comput+Sci 2021 ; 7 (ä): e345 Nephropedia Template TP {{tp|p=33834092|t=2021. A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images |pdf=|usr=}}{{33834092}} gab.com Text A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images JO: PeerJ Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33834092 #FOAvip BACKGROUND: COVID-19 pandemic imposed a lockdown situation to the world these past months. Researchers and scientists around the globe faced serious efforts from its detection to its treatment. METHODS: Pathogenic laboratory testing is the gold standard but it is time-consuming. Lung CT-scans and X-rays are other common methods applied by researchers to detect COVID-19 positive cases. In this paper, we propose a deep learning neural network-based model as an alternative fast screening method that can be used for detecting the COVID-19 cases by analyzing CT-scans. RESULTS: Applying the proposed method on a publicly available dataset collected of positive and negative cases showed its ability on distinguishing them by analyzing each individual CT image. The effect of different parameters on the performance of the proposed model was studied and tabulated. By selecting random train and test images, the overall accuracy and ROC-AUC of the proposed model can easily exceed 95% and 90%, respectively, without any image pre-selecting or preprocessing. Twit Text FOAVip A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images ????PeerJ Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33834092 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33834092 #FOAvip English Wikipedia <ref>{{Cite pmid|33834092}}</ref> A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images #MMPMID33834092 Mohammadpoor M; Sheikhi Karizaki M; Sheikhi Karizaki M PeerJ Comput Sci 2021[]; 7 (ä): e345 PMID33834092show ga BACKGROUND: COVID-19 pandemic imposed a lockdown situation to the world these past months. Researchers and scientists around the globe faced serious efforts from its detection to its treatment. METHODS: Pathogenic laboratory testing is the gold standard but it is time-consuming. Lung CT-scans and X-rays are other common methods applied by researchers to detect COVID-19 positive cases. In this paper, we propose a deep learning neural network-based model as an alternative fast screening method that can be used for detecting the COVID-19 cases by analyzing CT-scans. RESULTS: Applying the proposed method on a publicly available dataset collected of positive and negative cases showed its ability on distinguishing them by analyzing each individual CT image. The effect of different parameters on the performance of the proposed model was studied and tabulated. By selecting random train and test images, the overall accuracy and ROC-AUC of the proposed model can easily exceed 95% and 90%, respectively, without any image pre-selecting or preprocessing. äA deep learning algorithm to detect coronavirus (COVID-19) disease usi(epmc).pdf DeepDyve Pubget Overpricing