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10.1155/2021/3277988 http://scihub22266oqcxt.onion/10.1155/2021/3277988 34150188!8197673!34150188     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Healthc+Eng 2021 ; 2021 (ä): 3277988 Nephropedia Template TP {{tp|p=34150188|t=2021. Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases |pdf=|usr=}}{{34150188}} gab.com Text Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases JO: J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=34150188 #FOAvip The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients. Twit Text FOAVip Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases ????J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=34150188 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34150188 #FOAvip English Wikipedia <ref>{{Cite pmid|34150188}}</ref> Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases #MMPMID34150188 Iskanderani AI; Mehedi IM; Aljohani AJ; Shorfuzzaman M; Akther F; Palaniswamy T; Latif SA; Latif A; Alam A J Healthc Eng 2021[]; 2021 (ä): 3277988 PMID34150188show ga The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients. |*Artificial Intelligence[MESH] |*COVID-19 Testing[MESH] |*Internet of Things[MESH] |*SARS-CoV-2[MESH] |Brazil[MESH] |COVID-19/*diagnosis[MESH] |China[MESH] |Computer Simulation[MESH] |Computer Systems[MESH] |Databases, Factual[MESH] |Deep Learning[MESH] |Diagnosis, Computer-Assisted[MESH] |Humans[MESH] |Pattern Recognition, Automated[MESH] |Radiography, Thoracic[MESH] |United States[MESH]Artificial Intelligence and Medical Internet of Things Framework for D(epmc).pdf DeepDyve Pubget Overpricing