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10.1007/s13755-020-00135-3 http://scihub22266oqcxt.onion/10.1007/s13755-020-00135-3 33235710!7680558!33235710     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 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       Health+Inf+Sci+Syst 2021 ; 9 (1): 2 Nephropedia Template TP {{tp|p=33235710|t=2021. Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?|pdf=|usr=}}{{33235710}} gab.com Text Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning JO: Health Inf Sci Syst http://www.kidney.de/pget.php?&tw=1&pmid=33235710 #FOAvip BACKGROUND AND OBJECTIVES: Chest X-ray data have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. Given many new DL models have been being developed for this purpose, the objective of this study is to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. If fine-tuned pre-trained CNNs can provide equivalent or better classification results than other more sophisticated CNNs, then the deployment of AI-based tools for detecting COVID-19 using chest X-ray data can be more rapid and cost-effective. METHODS: Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases. RESULTS: In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver-operating-characteristic curve. CONCLUSION: AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation. Twit Text FOAVip Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning ????Health Inf Sci Syst http://www.kidney.de/pget.php?&tw=1&pmid=33235710 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33235710 #FOAvip English Wikipedia <ref>{{Cite pmid|33235710}}</ref> Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? #MMPMID33235710 Pham TD Health Inf Sci Syst 2021[Dec]; 9 (1): 2 PMID33235710show ga BACKGROUND AND OBJECTIVES: Chest X-ray data have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. Given many new DL models have been being developed for this purpose, the objective of this study is to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. If fine-tuned pre-trained CNNs can provide equivalent or better classification results than other more sophisticated CNNs, then the deployment of AI-based tools for detecting COVID-19 using chest X-ray data can be more rapid and cost-effective. METHODS: Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases. RESULTS: In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver-operating-characteristic curve. CONCLUSION: AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation. äClassification of COVID-19 chest X-rays with deep learning: new models(epmc).pdf DeepDyve Pubget Overpricing