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10.1016/j.clinimag.2021.07.004 http://scihub22266oqcxt.onion/10.1016/j.clinimag.2021.07.004 34425544!8302887!34425544     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Clin+Imaging 2021 ; 80 (ä): 268-273 Nephropedia Template TP {{tp|p=34425544|t=2021. Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays |pdf=|usr=}}{{34425544}} gab.com Text Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays JO: Clin Imaging http://www.kidney.de/pget.php?&tw=1&pmid=34425544 #FOAvip INTRODUCTION: The objective of this study was to assess seven configurations of six convolutional deep neural network architectures for classification of chest X-rays (CXRs) as COVID-19 positive or negative. METHODS: The primary dataset consisted of 294 COVID-19 positive and 294 COVID-19 negative CXRs, the latter comprising roughly equally many pneumonia, emphysema, fibrosis, and healthy images. We used six common convolutional neural network architectures, VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile and InceptionV3. We studied six models (one for each architecture) which were pre-trained on a vast repository of generic (non-CXR) images, as well as a seventh DenseNet121 model, which was pre-trained on a repository of CXR images. For each model, we replaced the output layers with custom fully connected layers for the task of binary classification of images as COVID-19 positive or negative. Performance metrics were calculated on a hold-out test set with CXRs from patients who were not included in the training/validation set. RESULTS: When pre-trained on generic images, the VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile, and InceptionV3 architectures respectively produced hold-out test set areas under the receiver operating characteristic (AUROCs) of 0.98, 0.95, 0.97, 0.95, 0.99, and 0.96 for the COVID-19 classification of CXRs. The X-ray pre-trained DenseNet121 model, in comparison, had a test set AUROC of 0.87. DISCUSSION: Common convolutional neural network architectures with parameters pre-trained on generic images yield high-performance and well-calibrated COVID-19 CXR classification. Twit Text FOAVip Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays ????Clin Imaging http://www.kidney.de/pget.php?&tw=1&pmid=34425544 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34425544 #FOAvip English Wikipedia <ref>{{Cite pmid|34425544}}</ref> Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays #MMPMID34425544 Maharjan J; Calvert J; Pellegrini E; Green-Saxena A; Hoffman J; McCoy A; Mao Q; Das R Clin Imaging 2021[Dec]; 80 (ä): 268-273 PMID34425544show ga INTRODUCTION: The objective of this study was to assess seven configurations of six convolutional deep neural network architectures for classification of chest X-rays (CXRs) as COVID-19 positive or negative. METHODS: The primary dataset consisted of 294 COVID-19 positive and 294 COVID-19 negative CXRs, the latter comprising roughly equally many pneumonia, emphysema, fibrosis, and healthy images. We used six common convolutional neural network architectures, VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile and InceptionV3. We studied six models (one for each architecture) which were pre-trained on a vast repository of generic (non-CXR) images, as well as a seventh DenseNet121 model, which was pre-trained on a repository of CXR images. For each model, we replaced the output layers with custom fully connected layers for the task of binary classification of images as COVID-19 positive or negative. Performance metrics were calculated on a hold-out test set with CXRs from patients who were not included in the training/validation set. RESULTS: When pre-trained on generic images, the VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile, and InceptionV3 architectures respectively produced hold-out test set areas under the receiver operating characteristic (AUROCs) of 0.98, 0.95, 0.97, 0.95, 0.99, and 0.96 for the COVID-19 classification of CXRs. The X-ray pre-trained DenseNet121 model, in comparison, had a test set AUROC of 0.87. DISCUSSION: Common convolutional neural network architectures with parameters pre-trained on generic images yield high-performance and well-calibrated COVID-19 CXR classification. |*COVID-19[MESH] |*Deep Learning[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |SARS-CoV-2[MESH]Application of deep learning to identify COVID-19 infection in postero(epmc).pdf DeepDyve Pubget Overpricing