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10.1016/j.compbiomed.2021.104453 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2021.104453 33957343!8087862!33957343     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Biol+Med 2021 ; 134 (ä): 104453 Nephropedia Template TP {{tp|p=33957343|t=2021. LBP-based information assisted intelligent system for COVID-19 identification |pdf=|usr=}}{{33957343}} gab.com Text LBP-based information assisted intelligent system for COVID-19 identification JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33957343 #FOAvip A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis. Twit Text FOAVip LBP-based information assisted intelligent system for COVID-19 identification ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33957343 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33957343 #FOAvip English Wikipedia <ref>{{Cite pmid|33957343}}</ref> LBP-based information assisted intelligent system for COVID-19 identification #MMPMID33957343 Maheshwari S; Sharma RR; Kumar M Comput Biol Med 2021[Jul]; 134 (ä): 104453 PMID33957343show ga A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis. |*COVID-19[MESH] |*Deep Learning[MESH] |Algorithms[MESH] |COVID-19 Testing[MESH] |Humans[MESH]LBP-based information assisted intelligent system for COVID-19 identif(epmc).pdf DeepDyve Pubget Overpricing