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.

10.1109/TNNLS.2021.3054306 http://scihub22266oqcxt.onion/10.1109/TNNLS.2021.3054306 33571095!8544942!33571095     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Neural+Netw+Learn+Syst 2021 ; 32 (4): 1408-1417 Nephropedia Template TP {{tp|p=33571095|t=2021. An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis |pdf=|usr=}}{{33571095}} gab.com Text An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis JO: IEEE Trans Neural Netw Learn Syst http://www.kidney.de/pget.php?&tw=1&pmid=33571095 #FOAvip The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries, and also, there are genuine concerns about their reliability and performance. Motivated by these shortcomings, this article proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images. Four popular convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet121, and InceptionResNetV2, are first applied to extract deep features from chest X-ray and computed tomography (CT) images. Extracted features are then processed by different machine learning and statistical modeling techniques to identify COVID-19 cases. We also calculate and report the epistemic uncertainty of classification results to identify regions where the trained models are not confident about their decisions (out of distribution problem). Comprehensive simulation results for X-ray and CT image data sets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). Also, it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images. Twit Text FOAVip An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis ????IEEE Trans Neural Netw Learn Syst http://www.kidney.de/pget.php?&tw=1&pmid=33571095 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33571095 #FOAvip English Wikipedia <ref>{{Cite pmid|33571095}}</ref> An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis #MMPMID33571095 Shamsi A; Asgharnezhad H; Jokandan SS; Khosravi A; Kebria PM; Nahavandi D; Nahavandi S; Srinivasan D IEEE Trans Neural Netw Learn Syst 2021[Apr]; 32 (4): 1408-1417 PMID33571095show ga The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries, and also, there are genuine concerns about their reliability and performance. Motivated by these shortcomings, this article proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images. Four popular convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet121, and InceptionResNetV2, are first applied to extract deep features from chest X-ray and computed tomography (CT) images. Extracted features are then processed by different machine learning and statistical modeling techniques to identify COVID-19 cases. We also calculate and report the epistemic uncertainty of classification results to identify regions where the trained models are not confident about their decisions (out of distribution problem). Comprehensive simulation results for X-ray and CT image data sets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). Also, it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images. |*Transfer, Psychology[MESH] |*Uncertainty[MESH] |Algorithms[MESH] |COVID-19 Testing/*methods[MESH] |COVID-19/*diagnosis/diagnostic imaging[MESH] |Computer Simulation[MESH] |Deep Learning[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Machine Learning[MESH] |Neural Networks, Computer[MESH] |ROC Curve[MESH] |Radiography, Thoracic[MESH] |Reproducibility of Results[MESH] |Sensitivity and Specificity[MESH] |Support Vector Machine[MESH] |Thorax/diagnostic imaging[MESH]An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Di(epmc).pdf DeepDyve Pubget Overpricing