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10.1016/j.bspc.2021.103076 http://scihub22266oqcxt.onion/10.1016/j.bspc.2021.103076 34457034!8384584!34457034     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Signal+Process+Control 2022 ; 71 (ä): 103076 Nephropedia Template TP {{tp|p=34457034|t=2022. COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning |pdf=|usr=}}{{34457034}} gab.com Text COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning JO: Biomed Signal Process Control http://www.kidney.de/pget.php?&tw=1&pmid=34457034 #FOAvip In the current scenario, novel coronavirus disease (COVID-19) spread is increasing day-by-day. It is very important to control and cure this disease. Reverse transcription-polymerase chain reaction (RT-PCR), chest computerized tomography (CT) imaging options are available as a significantly useful and more truthful tool to classify COVID-19 within the epidemic region. Most of the hospitals have CT imaging machines. It will be fruitful to utilize the chest CT images for early diagnosis and classification of COVID-19 patients. This requires a radiology expert and a good amount of time to classify the chest CT-based COVID-19 images especially when the disease is spreading at a rapid rate. During this pandemic COVID-19, there is a need for an efficient automated way to check for infection. CT is one of the best ways to detect infection inpatients. This paper introduces a new method for preprocessing and classifying COVID-19 positive and negative from CT scan images. The method which is being proposed uses the concept of empirical wavelet transformation for preprocessing, selecting the best components of the red, green, and blue channels of the image are trained on the proposed network. With the proposed methodology, the classification accuracy of 85.5%, F1 score of 85.28%, and AUC of 96.6% are achieved. Twit Text FOAVip COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning ????Biomed Signal Process Control http://www.kidney.de/pget.php?&tw=1&pmid=34457034 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34457034 #FOAvip English Wikipedia <ref>{{Cite pmid|34457034}}</ref> COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning #MMPMID34457034 Gaur P; Malaviya V; Gupta A; Bhatia G; Pachori RB; Sharma D Biomed Signal Process Control 2022[Jan]; 71 (ä): 103076 PMID34457034show ga In the current scenario, novel coronavirus disease (COVID-19) spread is increasing day-by-day. It is very important to control and cure this disease. Reverse transcription-polymerase chain reaction (RT-PCR), chest computerized tomography (CT) imaging options are available as a significantly useful and more truthful tool to classify COVID-19 within the epidemic region. Most of the hospitals have CT imaging machines. It will be fruitful to utilize the chest CT images for early diagnosis and classification of COVID-19 patients. This requires a radiology expert and a good amount of time to classify the chest CT-based COVID-19 images especially when the disease is spreading at a rapid rate. During this pandemic COVID-19, there is a need for an efficient automated way to check for infection. CT is one of the best ways to detect infection inpatients. This paper introduces a new method for preprocessing and classifying COVID-19 positive and negative from CT scan images. The method which is being proposed uses the concept of empirical wavelet transformation for preprocessing, selecting the best components of the red, green, and blue channels of the image are trained on the proposed network. With the proposed methodology, the classification accuracy of 85.5%, F1 score of 85.28%, and AUC of 96.6% are achieved. äCOVID-19 disease identification from chest CT images using empirical w(epmc).pdf DeepDyve Pubget Overpricing