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10.1186/s12880-020-00529-5 http://scihub22266oqcxt.onion/10.1186/s12880-020-00529-5 33557772!7870362!33557772     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Imaging 2021 ; 21 (1): 19 Nephropedia Template TP {{tp|p=33557772|t=2021. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet |pdf=|usr=}}{{33557772}} gab.com Text COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet JO: BMC Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33557772 #FOAvip BACKGROUND: Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, SegNet and U-NET, are investigated for semantically segmenting infected tissue regions in CT lung images. METHODS: We propose to use two known deep learning networks, SegNet and U-NET, for image tissue classification. SegNet is characterized as a scene segmentation network and U-NET as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly. RESULTS: The results show the superior ability of SegNet in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the U-NET shows better results as a multi-class segmentor (with 0.91 mean accuracy). CONCLUSION: Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today's pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally. Twit Text FOAVip COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet ????BMC Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33557772 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33557772 #FOAvip English Wikipedia <ref>{{Cite pmid|33557772}}</ref> COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet #MMPMID33557772 Saood A; Hatem I BMC Med Imaging 2021[Feb]; 21 (1): 19 PMID33557772show ga BACKGROUND: Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, SegNet and U-NET, are investigated for semantically segmenting infected tissue regions in CT lung images. METHODS: We propose to use two known deep learning networks, SegNet and U-NET, for image tissue classification. SegNet is characterized as a scene segmentation network and U-NET as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly. RESULTS: The results show the superior ability of SegNet in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the U-NET shows better results as a multi-class segmentor (with 0.91 mean accuracy). CONCLUSION: Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today's pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally. |COVID-19/*diagnostic imaging[MESH] |Deep Learning[MESH] |Humans[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH]COVID-19 lung CT image segmentation using deep learning methods: U-Net(epmc).pdf DeepDyve Pubget Overpricing