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10.1177/2472630320958376 http://scihub22266oqcxt.onion/10.1177/2472630320958376 32948098!7502682!32948098     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       SLAS+Technol 2020 ; 25 (6): 553-565 Nephropedia Template TP {{tp|p=32948098|t=2020. Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks |pdf=|usr=}}{{32948098}} gab.com Text Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks JO: SLAS Technol http://www.kidney.de/pget.php?&tw=1&pmid=32948098 #FOAvip The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics-area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images. Twit Text FOAVip Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks ????SLAS Technol http://www.kidney.de/pget.php?&tw=1&pmid=32948098 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32948098 #FOAvip English Wikipedia <ref>{{Cite pmid|32948098}}</ref> Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks #MMPMID32948098 Sekeroglu B; Ozsahin I SLAS Technol 2020[Dec]; 25 (6): 553-565 PMID32948098show ga The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics-area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images. |Adult[MESH] |Aged[MESH] |COVID-19/*diagnosis[MESH] |Computer Simulation[MESH] |Diagnostic Imaging/*methods[MESH] |Female[MESH] |Humans[MESH] |Image Processing, Computer-Assisted/*methods[MESH] |Lung/*diagnostic imaging[MESH] |Machine Learning[MESH] |Male[MESH] |Middle Aged[MESH] |Neural Networks, Computer[MESH] |Reproducibility of Results[MESH] |SARS-CoV-2/*physiology[MESH] |Sensitivity and Specificity[MESH]Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neur(epmc).pdf DeepDyve Pubget Overpricing