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.

10.3390/s21020455 http://scihub22266oqcxt.onion/10.3390/s21020455 33440674!7828058!33440674     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       Sensors+(Basel) 2021 ; 21 (2): ä Nephropedia Template TP {{tp|p=33440674|t=2021. Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning |pdf=|usr=}}{{33440674}} gab.com Text Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33440674 #FOAvip This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models' predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists. Twit Text FOAVip Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33440674 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33440674 #FOAvip English Wikipedia <ref>{{Cite pmid|33440674}}</ref> Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning #MMPMID33440674 Alshazly H; Linse C; Barth E; Martinetz T Sensors (Basel) 2021[Jan]; 21 (2): ä PMID33440674show ga This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models' predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists. |*Deep Learning[MESH] |*Tomography, X-Ray Computed[MESH] |Algorithms[MESH] |COVID-19/*diagnosis/diagnostic imaging/virology[MESH] |Databases, Factual[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |Radiographic Image Interpretation, Computer-Assisted[MESH] |SARS-CoV-2/pathogenicity[MESH]Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning (epmc).pdf DeepDyve Pubget Overpricing