Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1007/s13246-020-00952-6 http://scihub22266oqcxt.onion/10.1007/s13246-020-00952-6 33275187!7715648!33275187     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 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 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 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Phys+Eng+Sci+Med 2020 ; 43 (4): 1399-1414 Nephropedia Template TP {{tp|p=33275187|t=2020. StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images |pdf=|usr=}}{{33275187}} gab.com Text StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images JO: Phys Eng Sci Med http://www.kidney.de/pget.php?&tw=1&pmid=33275187 #FOAvip The highly contagious nature of Coronavirus disease 2019 (Covid-19) resulted in a global pandemic. Due to the relatively slow and taxing nature of conventional testing for Covid-19, a faster method needs to be in place. The current researches have suggested that visible irregularities found in the chest X-ray of Covid-19 positive patients are indicative of the presence of the disease. Hence, Deep Learning and Image Classification techniques can be employed to learn from these irregularities, and classify accordingly with high accuracy. This research presents an approach to create a classifier model named StackNet-DenVIS which is designed to act as a screening process before conducting the existing swab tests. Using a novel approach, which incorporates Transfer Learning and Stacked Generalization, the model aims to lower the False Negative rate of classification compensating for the 30% False Negative rate of the swab tests. A dataset gathered from multiple reliable sources consisting of 9953 Chest X-rays (868 Covid and 9085 Non-Covid) was used. Also, this research demonstrates handling data imbalance using various techniques involving Generative Adversarial Networks and sampling techniques. The accuracy, sensitivity, and specificity obtained on our proposed model were 95.07%, 99.40% and 94.61% respectively. To the best of our knowledge, the combination of accuracy and false negative rate obtained by this paper outperforms the current implementations. We must also highlight that our proposed architecture also considers other types of viral pneumonia. Given the unprecedented sensitivity of our model we are optimistic it contributes to a better Covid-19 detection. Twit Text FOAVip StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images ????Phys Eng Sci Med http://www.kidney.de/pget.php?&tw=1&pmid=33275187 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33275187 #FOAvip English Wikipedia <ref>{{Cite pmid|33275187}}</ref> StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images #MMPMID33275187 Autee P; Bagwe S; Shah V; Srivastava K Phys Eng Sci Med 2020[Dec]; 43 (4): 1399-1414 PMID33275187show ga The highly contagious nature of Coronavirus disease 2019 (Covid-19) resulted in a global pandemic. Due to the relatively slow and taxing nature of conventional testing for Covid-19, a faster method needs to be in place. The current researches have suggested that visible irregularities found in the chest X-ray of Covid-19 positive patients are indicative of the presence of the disease. Hence, Deep Learning and Image Classification techniques can be employed to learn from these irregularities, and classify accordingly with high accuracy. This research presents an approach to create a classifier model named StackNet-DenVIS which is designed to act as a screening process before conducting the existing swab tests. Using a novel approach, which incorporates Transfer Learning and Stacked Generalization, the model aims to lower the False Negative rate of classification compensating for the 30% False Negative rate of the swab tests. A dataset gathered from multiple reliable sources consisting of 9953 Chest X-rays (868 Covid and 9085 Non-Covid) was used. Also, this research demonstrates handling data imbalance using various techniques involving Generative Adversarial Networks and sampling techniques. The accuracy, sensitivity, and specificity obtained on our proposed model were 95.07%, 99.40% and 94.61% respectively. To the best of our knowledge, the combination of accuracy and false negative rate obtained by this paper outperforms the current implementations. We must also highlight that our proposed architecture also considers other types of viral pneumonia. Given the unprecedented sensitivity of our model we are optimistic it contributes to a better Covid-19 detection. |*Algorithms[MESH] |*COVID-19 Testing[MESH] |*Neural Networks, Computer[MESH] |Artifacts[MESH] |COVID-19/*diagnosis/*diagnostic imaging/virology[MESH] |Databases, Factual[MESH] |Humans[MESH] |Image Processing, Computer-Assisted[MESH] |Lung/diagnostic imaging[MESH] |Models, Theoretical[MESH] |ROC Curve[MESH] |SARS-CoV-2/physiology[MESH] |Time Factors[MESH]StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach (epmc).pdf DeepDyve Pubget Overpricing