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10.3390/s21051742 http://scihub22266oqcxt.onion/10.3390/s21051742 33802428!7959300!33802428     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       Sensors+(Basel) 2021 ; 21 (5): ä Nephropedia Template TP {{tp|p=33802428|t=2021. COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases |pdf=|usr=}}{{33802428}} gab.com Text COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33802428 #FOAvip The recognition of COVID-19 infection from X-ray images is an emerging field in the learning and computer vision community. Despite the great efforts that have been made in this field since the appearance of COVID-19 (2019), the field still suffers from two drawbacks. First, the number of available X-ray scans labeled as COVID-19-infected is relatively small. Second, all the works that have been carried out in the field are separate; there are no unified data, classes, and evaluation protocols. In this work, based on public and newly collected data, we propose two X-ray COVID-19 databases, which are three-class COVID-19 and five-class COVID-19 datasets. For both databases, we evaluate different deep learning architectures. Moreover, we propose an Ensemble-CNNs approach which outperforms the deep learning architectures and shows promising results in both databases. In other words, our proposed Ensemble-CNNs achieved a high performance in the recognition of COVID-19 infection, resulting in accuracies of 100% and 98.1% in the three-class and five-class scenarios, respectively. In addition, our approach achieved promising results in the overall recognition accuracy of 75.23% and 81.0% for the three-class and five-class scenarios, respectively. We make our databases of COVID-19 X-ray scans publicly available to encourage other researchers to use it as a benchmark for their studies and comparisons. Twit Text FOAVip COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33802428 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33802428 #FOAvip English Wikipedia <ref>{{Cite pmid|33802428}}</ref> COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases #MMPMID33802428 Vantaggiato E; Paladini E; Bougourzi F; Distante C; Hadid A; Taleb-Ahmed A Sensors (Basel) 2021[Mar]; 21 (5): ä PMID33802428show ga The recognition of COVID-19 infection from X-ray images is an emerging field in the learning and computer vision community. Despite the great efforts that have been made in this field since the appearance of COVID-19 (2019), the field still suffers from two drawbacks. First, the number of available X-ray scans labeled as COVID-19-infected is relatively small. Second, all the works that have been carried out in the field are separate; there are no unified data, classes, and evaluation protocols. In this work, based on public and newly collected data, we propose two X-ray COVID-19 databases, which are three-class COVID-19 and five-class COVID-19 datasets. For both databases, we evaluate different deep learning architectures. Moreover, we propose an Ensemble-CNNs approach which outperforms the deep learning architectures and shows promising results in both databases. In other words, our proposed Ensemble-CNNs achieved a high performance in the recognition of COVID-19 infection, resulting in accuracies of 100% and 98.1% in the three-class and five-class scenarios, respectively. In addition, our approach achieved promising results in the overall recognition accuracy of 75.23% and 81.0% for the three-class and five-class scenarios, respectively. We make our databases of COVID-19 X-ray scans publicly available to encourage other researchers to use it as a benchmark for their studies and comparisons. |*Deep Learning[MESH] |*Neural Networks, Computer[MESH] |*Radiography, Thoracic[MESH] |Algorithms[MESH] |COVID-19/*diagnostic imaging[MESH] |Humans[MESH]COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databa(epmc).pdf DeepDyve Pubget Overpricing