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10.1117/1.JMI.8.S1.014502 http://scihub22266oqcxt.onion/10.1117/1.JMI.8.S1.014502 33912622!8071782!33912622     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Med+Imaging+(Bellingham) 2021 ; 8 (Suppl 1): 014502 Nephropedia Template TP {{tp|p=33912622|t=2021. Deep CNN models for predicting COVID-19 in CT and x-ray images |pdf=|usr=}}{{33912622}} gab.com Text Deep CNN models for predicting COVID-19 in CT and x-ray images JO: J Med Imaging (Bellingham) http://www.kidney.de/pget.php?&tw=1&pmid=33912622 #FOAvip Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model's ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC). Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images. Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management. Twit Text FOAVip Deep CNN models for predicting COVID-19 in CT and x-ray images ????J Med Imaging (Bellingham) http://www.kidney.de/pget.php?&tw=1&pmid=33912622 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33912622 #FOAvip English Wikipedia <ref>{{Cite pmid|33912622}}</ref> Deep CNN models for predicting COVID-19 in CT and x-ray images #MMPMID33912622 Chaddad A; Hassan L; Desrosiers C J Med Imaging (Bellingham) 2021[Jan]; 8 (Suppl 1): 014502 PMID33912622show ga Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model's ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC). Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images. Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management. äDeep CNN models for predicting COVID-19 in CT and x-ray images (epmc).pdf DeepDyve Pubget Overpricing