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10.1016/j.bspc.2021.102656 http://scihub22266oqcxt.onion/10.1016/j.bspc.2021.102656 33897803!8057743!33897803     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Signal+Process+Control 2021 ; 68 (ä): 102656 Nephropedia Template TP {{tp|p=33897803|t=2021. Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images |pdf=|usr=}}{{33897803}} gab.com Text Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images JO: Biomed Signal Process Control http://www.kidney.de/pget.php?&tw=1&pmid=33897803 #FOAvip The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients' confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers. Twit Text FOAVip Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images ????Biomed Signal Process Control http://www.kidney.de/pget.php?&tw=1&pmid=33897803 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33897803 #FOAvip English Wikipedia <ref>{{Cite pmid|33897803}}</ref> Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images #MMPMID33897803 Ashour AS; Eissa MM; Wahba MA; Elsawy RA; Elgnainy HF; Tolba MS; Mohamed WS Biomed Signal Process Control 2021[Jul]; 68 (ä): 102656 PMID33897803show ga The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients' confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers. äEnsemble-based bag of features for automated classification of normal (epmc).pdf DeepDyve Pubget Overpricing