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10.1016/j.inffus.2021.02.013 http://scihub22266oqcxt.onion/10.1016/j.inffus.2021.02.013 33649704!7904462!33649704     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Inf+Fusion 2021 ; 72 (ä): 80-88 Nephropedia Template TP {{tp|p=33649704|t=2021. COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images |pdf=|usr=}}{{33649704}} gab.com Text COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images JO: Inf Fusion http://www.kidney.de/pget.php?&tw=1&pmid=33649704 #FOAvip COVID-19 or related viral pandemics should be detected and managed without hesitation, since the virus spreads very rapidly. Often with insufficient human and electronic resources, patients need to be checked from stable patients using vital signs, radiographic photographs, or ultrasound images. Vital signs do not often offer the right outcome, and radiographic photos have a variety of other problems. Lung ultrasound (LUS) images can provide good screening without a lot of complications. This paper suggests a model of a convolutionary neural network (CNN) that has fewer learning parameters but can achieve strong accuracy. The model has five main blocks or layers of convolution connectors. A multi-layer fusion functionality of each block is proposed to improve the efficiency of the COVID-19 screening method utilizing the proposed model. Experiments are conducted using freely accessible LUS photographs and video datasets. The proposed fusion method has 92.5% precision, 91.8% accuracy, and 93.2% retrieval using the data collection. These efficiency metric levels are considerably higher than those used in any of the state-of-the-art CNN versions. Twit Text FOAVip COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images ????Inf Fusion http://www.kidney.de/pget.php?&tw=1&pmid=33649704 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33649704 #FOAvip English Wikipedia <ref>{{Cite pmid|33649704}}</ref> COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images #MMPMID33649704 Muhammad G; Shamim Hossain M Inf Fusion 2021[Aug]; 72 (ä): 80-88 PMID33649704show ga COVID-19 or related viral pandemics should be detected and managed without hesitation, since the virus spreads very rapidly. Often with insufficient human and electronic resources, patients need to be checked from stable patients using vital signs, radiographic photographs, or ultrasound images. Vital signs do not often offer the right outcome, and radiographic photos have a variety of other problems. Lung ultrasound (LUS) images can provide good screening without a lot of complications. This paper suggests a model of a convolutionary neural network (CNN) that has fewer learning parameters but can achieve strong accuracy. The model has five main blocks or layers of convolution connectors. A multi-layer fusion functionality of each block is proposed to improve the efficiency of the COVID-19 screening method utilizing the proposed model. Experiments are conducted using freely accessible LUS photographs and video datasets. The proposed fusion method has 92.5% precision, 91.8% accuracy, and 93.2% retrieval using the data collection. These efficiency metric levels are considerably higher than those used in any of the state-of-the-art CNN versions. äCOVID-19 and Non-COVID-19 Classification using Multi-layers Fusion Fro(epmc).pdf DeepDyve Pubget Overpricing