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10.7717/peerj-cs.349 http://scihub22266oqcxt.onion/10.7717/peerj-cs.349 33816999!7924694!33816999     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PeerJ+Comput+Sci 2021 ; 7 (ä): e349 Nephropedia Template TP {{tp|p=33816999|t=2021. ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans |pdf=|usr=}}{{33816999}} gab.com Text ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans JO: PeerJ Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33816999 #FOAvip Currently, the new coronavirus disease (COVID-19) is one of the biggest health crises threatening the world. Automatic detection from computed tomography (CT) scans is a classic method to detect lung infection, but it faces problems such as high variations in intensity, indistinct edges near lung infected region and noise due to data acquisition process. Therefore, this article proposes a new COVID-19 pulmonary infection segmentation depth network referred as the Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET). The dense network replaces convolution and maximum pooling function to enhance feature propagation and solves gradient disappearance problem. An improved dilation convolution is used to increase the receptive field of the encoder output to further obtain more edge features from the small infected regions. The integration of attention gate into the model suppresses the background and improves prediction accuracy. The experimental results show that the ADID-UNET model can accurately segment COVID-19 lung infected areas, with performance measures greater than 80% for metrics like Accuracy, Specificity and Dice Coefficient (DC). Further when compared to other state-of-the-art architectures, the proposed model showed excellent segmentation effects with a high DC and F1 score of 0.8031 and 0.82 respectively. Twit Text FOAVip ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans ????PeerJ Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33816999 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33816999 #FOAvip English Wikipedia <ref>{{Cite pmid|33816999}}</ref> ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans #MMPMID33816999 Joseph Raj AN; Zhu H; Khan A; Zhuang Z; Yang Z; Mahesh VGV; Karthik G PeerJ Comput Sci 2021[]; 7 (ä): e349 PMID33816999show ga Currently, the new coronavirus disease (COVID-19) is one of the biggest health crises threatening the world. Automatic detection from computed tomography (CT) scans is a classic method to detect lung infection, but it faces problems such as high variations in intensity, indistinct edges near lung infected region and noise due to data acquisition process. Therefore, this article proposes a new COVID-19 pulmonary infection segmentation depth network referred as the Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET). The dense network replaces convolution and maximum pooling function to enhance feature propagation and solves gradient disappearance problem. An improved dilation convolution is used to increase the receptive field of the encoder output to further obtain more edge features from the small infected regions. The integration of attention gate into the model suppresses the background and improves prediction accuracy. The experimental results show that the ADID-UNET model can accurately segment COVID-19 lung infected areas, with performance measures greater than 80% for metrics like Accuracy, Specificity and Dice Coefficient (DC). Further when compared to other state-of-the-art architectures, the proposed model showed excellent segmentation effects with a high DC and F1 score of 0.8031 and 0.82 respectively. äADID-UNET-a segmentation model for COVID-19 infection from lung CT sca(epmc).pdf DeepDyve Pubget Overpricing