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10.1155/2021/5528441 http://scihub22266oqcxt.onion/10.1155/2021/5528441 33936577!8061232!33936577     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       J+Healthc+Eng 2021 ; 2021 (ä): 5528441 Nephropedia Template TP {{tp|p=33936577|t=2021. Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning |pdf=|usr=}}{{33936577}} gab.com Text Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning JO: J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=33936577 #FOAvip Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks. Twit Text FOAVip Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning ????J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=33936577 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33936577 #FOAvip English Wikipedia <ref>{{Cite pmid|33936577}}</ref> Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning #MMPMID33936577 Li X; Tan W; Liu P; Zhou Q; Yang J J Healthc Eng 2021[]; 2021 (ä): 5528441 PMID33936577show ga Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks. |*Deep Learning[MESH] |*Tomography, X-Ray Computed/classification/methods[MESH] |Algorithms[MESH] |COVID-19/*diagnostic imaging[MESH] |Databases, Factual[MESH] |Humans[MESH] |Pandemics[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH] |Radiography, Thoracic[MESH] |SARS-CoV-2[MESH]Classification of COVID-19 Chest CT Images Based on Ensemble Deep Lear(epmc).pdf DeepDyve Pubget Overpricing