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10.1016/j.chaos.2021.111310 http://scihub22266oqcxt.onion/10.1016/j.chaos.2021.111310 34376926!8339545!34376926     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 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 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos+Solitons+Fractals 2021 ; 151 (ä): 111310 Nephropedia Template TP {{tp|p=34376926|t=2021. C+EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT images based on CBAM and EfficientNet |pdf=|usr=}}{{34376926}} gab.com Text C+EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT images based on CBAM and EfficientNet JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=34376926 #FOAvip COVID-19, one of the biggest diseases of our age, continues to spread rapidly around the world. Studies continue rapidly for the diagnosis and treatment of this disease. It is of great importance that individuals who are infected with this virus be isolated from the rest of the society so that the disease does not spread further. In addition to the tests performed in the detection process of the patients, X-ray and computed tomography are also used. In this study, a new hybrid model that can diagnose COVID-19 from computed tomography images created using EfficientNet, one of the current deep learning models, with a model consisting of attention blocks is proposed. In the first step of this new model, channel attention, spatial attention, and residual blocks are used to extract the most important features from the images. The extracted features are combined in accordance with the hyper-column technique. The combined features are given as input to the EfficientNet models in the second step of the model. The deep features obtained from this proposed hybrid model were classified with the Support Vector Machine classifier after feature selection. Principal Components Analysis was used for feature selection. The approach can accurately predict COVID-19 with a 99% accuracy rate. The first four versions of EfficientNet are used in the approach. In addition, Bayesian optimization was used in the hyper parameter estimation of the Support Vector Machine classifier. Comparative performance analysis of the approach with other approaches in the field is given. Twit Text FOAVip C+EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT images based on CBAM and EfficientNet ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=34376926 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34376926 #FOAvip English Wikipedia <ref>{{Cite pmid|34376926}}</ref> C+EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT images based on CBAM and EfficientNet #MMPMID34376926 Canayaz M Chaos Solitons Fractals 2021[Oct]; 151 (ä): 111310 PMID34376926show ga COVID-19, one of the biggest diseases of our age, continues to spread rapidly around the world. Studies continue rapidly for the diagnosis and treatment of this disease. It is of great importance that individuals who are infected with this virus be isolated from the rest of the society so that the disease does not spread further. In addition to the tests performed in the detection process of the patients, X-ray and computed tomography are also used. In this study, a new hybrid model that can diagnose COVID-19 from computed tomography images created using EfficientNet, one of the current deep learning models, with a model consisting of attention blocks is proposed. In the first step of this new model, channel attention, spatial attention, and residual blocks are used to extract the most important features from the images. The extracted features are combined in accordance with the hyper-column technique. The combined features are given as input to the EfficientNet models in the second step of the model. The deep features obtained from this proposed hybrid model were classified with the Support Vector Machine classifier after feature selection. Principal Components Analysis was used for feature selection. The approach can accurately predict COVID-19 with a 99% accuracy rate. The first four versions of EfficientNet are used in the approach. In addition, Bayesian optimization was used in the hyper parameter estimation of the Support Vector Machine classifier. Comparative performance analysis of the approach with other approaches in the field is given. äC+EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT images(epmc).pdf DeepDyve Pubget Overpricing