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10.1155/2021/3604900 http://scihub22266oqcxt.onion/10.1155/2021/3604900 34239548!8214492!34239548     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34239548&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       Comput+Intell+Neurosci 2021 ; 2021 (ä): 3604900 Nephropedia Template TP {{tp|p=34239548|t=2021. Detecting COVID-19 in Chest X-Ray Images via MCFF-Net |pdf=|usr=}}{{34239548}} gab.com Text Detecting COVID-19 in Chest X-Ray Images via MCFF-Net JO: Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=34239548 #FOAvip COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). Due to the rapid spread of COVID-19 around the world, the number of COVID-19 cases continues to increase, and lots of countries are facing tremendous pressure on both public and medical resources. Although RT-PCR is the most widely used detection technology with COVID-19 detection, it still has some limitations, such as high cost, being time-consuming, and having low sensitivity. According to the characteristics of chest X-ray (CXR) images, we design the Parallel Channel Attention Feature Fusion Module (PCAF), as well as a new structure of convolutional neural network MCFF-Net proposed based on PCAF. In order to improve the recognition efficiency, the network adopts 3 classifiers: 1-FC, GAP-FC, and Conv1-GAP. The experimental results show that the overall accuracy of MCFF-Net66-Conv1-GAP model is 94.66% for 4-class classification. Simultaneously, the classification accuracy, precision, sensitivity, specificity, and F1-score of COVID-19 are 100%. MCFF-Net may not only assist clinicians in making appropriate decisions for COVID-19 diagnosis but also mitigate the lack of testing kits. Twit Text FOAVip Detecting COVID-19 in Chest X-Ray Images via MCFF-Net ????Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=34239548 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34239548 #FOAvip English Wikipedia <ref>{{Cite pmid|34239548}}</ref> Detecting COVID-19 in Chest X-Ray Images via MCFF-Net #MMPMID34239548 Wang W; Li Y; Li J; Zhang P; Wang X Comput Intell Neurosci 2021[]; 2021 (ä): 3604900 PMID34239548show ga COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). Due to the rapid spread of COVID-19 around the world, the number of COVID-19 cases continues to increase, and lots of countries are facing tremendous pressure on both public and medical resources. Although RT-PCR is the most widely used detection technology with COVID-19 detection, it still has some limitations, such as high cost, being time-consuming, and having low sensitivity. According to the characteristics of chest X-ray (CXR) images, we design the Parallel Channel Attention Feature Fusion Module (PCAF), as well as a new structure of convolutional neural network MCFF-Net proposed based on PCAF. In order to improve the recognition efficiency, the network adopts 3 classifiers: 1-FC, GAP-FC, and Conv1-GAP. The experimental results show that the overall accuracy of MCFF-Net66-Conv1-GAP model is 94.66% for 4-class classification. Simultaneously, the classification accuracy, precision, sensitivity, specificity, and F1-score of COVID-19 are 100%. MCFF-Net may not only assist clinicians in making appropriate decisions for COVID-19 diagnosis but also mitigate the lack of testing kits. |*COVID-19[MESH] |*Deep Learning[MESH] |COVID-19 Testing[MESH] |Humans[MESH] |SARS-CoV-2[MESH]Detecting COVID-19 in Chest X-Ray Images via MCFF-Net (epmc).pdf DeepDyve Pubget Overpricing