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10.1007/s12539-021-00418-7 http://scihub22266oqcxt.onion/10.1007/s12539-021-00418-7 33587262!7882874!33587262     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 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 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Interdiscip+Sci 2021 ; 13 (2): 260-272 Nephropedia Template TP {{tp|p=33587262|t=2021. DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images |pdf=|usr=}}{{33587262}} gab.com Text DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images JO: Interdiscip Sci http://www.kidney.de/pget.php?&tw=1&pmid=33587262 #FOAvip In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading among individuals. This article proposes multi-objective optimization and a deep-learning methodology for the detection of infected coronavirus patients with X-rays. J48 decision tree method classifies the deep characteristics of affected X-ray corona images to detect the contaminated patients effectively. Eleven different convolutional neuronal network-based (CNN) models were developed in this study to detect infected patients with coronavirus pneumonia using X-ray images (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet500, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet). In addition, the parameters of the CNN profound learning model are described using an emperor penguin optimizer with several objectives (MOEPO). A broad review reveals that the proposed model can categorise the X-ray images at the correct rates of precision, accuracy, recall, specificity and F1-score. Extensive test results show that the proposed model outperforms competitive models with well-known efficiency metrics. The proposed model is, therefore, useful for the real-time classification of X-ray chest images of COVID-19 disease. Twit Text FOAVip DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images ????Interdiscip Sci http://www.kidney.de/pget.php?&tw=1&pmid=33587262 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33587262 #FOAvip English Wikipedia <ref>{{Cite pmid|33587262}}</ref> DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images #MMPMID33587262 Dhiman G; Vinoth Kumar V; Kaur A; Sharma A Interdiscip Sci 2021[Jun]; 13 (2): 260-272 PMID33587262show ga In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading among individuals. This article proposes multi-objective optimization and a deep-learning methodology for the detection of infected coronavirus patients with X-rays. J48 decision tree method classifies the deep characteristics of affected X-ray corona images to detect the contaminated patients effectively. Eleven different convolutional neuronal network-based (CNN) models were developed in this study to detect infected patients with coronavirus pneumonia using X-ray images (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet500, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet). In addition, the parameters of the CNN profound learning model are described using an emperor penguin optimizer with several objectives (MOEPO). A broad review reveals that the proposed model can categorise the X-ray images at the correct rates of precision, accuracy, recall, specificity and F1-score. Extensive test results show that the proposed model outperforms competitive models with well-known efficiency metrics. The proposed model is, therefore, useful for the real-time classification of X-ray chest images of COVID-19 disease. |*Deep Learning[MESH] |*Diagnosis, Computer-Assisted[MESH] |*Radiographic Image Interpretation, Computer-Assisted[MESH] |*Radiography, Thoracic[MESH] |COVID-19/*diagnostic imaging/virology[MESH] |Decision Trees[MESH] |Humans[MESH] |Lung/*diagnostic imaging/virology[MESH] |Predictive Value of Tests[MESH]DON: Deep Learning and Optimization-Based Framework for Detection of N(epmc).pdf DeepDyve Pubget Overpricing