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10.1016/j.imu.2020.100506 http://scihub22266oqcxt.onion/10.1016/j.imu.2020.100506 33392388!7759122!33392388     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33392388&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Inform+Med+Unlocked 2021 ; 22 (ä): 100506 Nephropedia Template TP {{tp|p=33392388|t=2021. FCOD: Fast COVID-19 Detector based on deep learning techniques |pdf=|usr=}}{{33392388}} gab.com Text FCOD: Fast COVID-19 Detector based on deep learning techniques JO: Inform Med Unlocked http://www.kidney.de/pget.php?&tw=1&pmid=33392388 #FOAvip The sudden COVID-19 pandemic has caused a serious global concern due to infections and mortality rates. It is a hazardous disease that has recently become the biggest crisis in the modern era. Due to the limitation of test kits and the need for screening and rapid diagnosis of patients, it is essential to perform a self-operating detection model as a fast recognition system to detect COVID-19 infection and prevent the spread among the people. In this paper, we propose a novel technique called Fast COVID-19 Detector (FCOD) to have a fast detection of COVID-19 using X-ray images. The FCOD is a deep learning model based on the Inception architecture that uses 17 depthwise separable convolution layers to detect COVID-19. Depthwise separable convolution layers decrease the computation costs, time, and they can have a reducing role in the number of parameters compared to the standard convolution layers. To evaluate FCOD, we used covid-chestxray-dataset, which contains 940 publicly available typical chest X-ray images. Our results show that FCOD can provide accuracy, F1-score, and AUC of 96%, 96%, and 0.95%, respectively in classifying COVID-19 during 0.014 s for each case. The proposed model can be employed as a supportive decision-making system to assist radiologists in clinics and hospitals to screen patients immediately. Twit Text FOAVip FCOD: Fast COVID-19 Detector based on deep learning techniques ????Inform Med Unlocked http://www.kidney.de/pget.php?&tw=1&pmid=33392388 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33392388 #FOAvip English Wikipedia <ref>{{Cite pmid|33392388}}</ref> FCOD: Fast COVID-19 Detector based on deep learning techniques #MMPMID33392388 Panahi AH; Rafiei A; Rezaee A Inform Med Unlocked 2021[]; 22 (ä): 100506 PMID33392388show ga The sudden COVID-19 pandemic has caused a serious global concern due to infections and mortality rates. It is a hazardous disease that has recently become the biggest crisis in the modern era. Due to the limitation of test kits and the need for screening and rapid diagnosis of patients, it is essential to perform a self-operating detection model as a fast recognition system to detect COVID-19 infection and prevent the spread among the people. In this paper, we propose a novel technique called Fast COVID-19 Detector (FCOD) to have a fast detection of COVID-19 using X-ray images. The FCOD is a deep learning model based on the Inception architecture that uses 17 depthwise separable convolution layers to detect COVID-19. Depthwise separable convolution layers decrease the computation costs, time, and they can have a reducing role in the number of parameters compared to the standard convolution layers. To evaluate FCOD, we used covid-chestxray-dataset, which contains 940 publicly available typical chest X-ray images. Our results show that FCOD can provide accuracy, F1-score, and AUC of 96%, 96%, and 0.95%, respectively in classifying COVID-19 during 0.014 s for each case. The proposed model can be employed as a supportive decision-making system to assist radiologists in clinics and hospitals to screen patients immediately. äFCOD: Fast COVID-19 Detector based on deep learning techniques (epmc).pdf DeepDyve Pubget Overpricing