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10.1007/s13735-021-00204-7 http://scihub22266oqcxt.onion/10.1007/s13735-021-00204-7 33643764!7896551!33643764     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       Int+J+Multimed+Inf+Retr 2021 ; 10 (1): 55-68 Nephropedia Template TP {{tp|p=33643764|t=2021. Design ensemble deep learning model for pneumonia disease classification |pdf=|usr=}}{{33643764}} gab.com Text Design ensemble deep learning model for pneumonia disease classification JO: Int J Multimed Inf Retr http://www.kidney.de/pget.php?&tw=1&pmid=33643764 #FOAvip With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score). Twit Text FOAVip Design ensemble deep learning model for pneumonia disease classification ????Int J Multimed Inf Retr http://www.kidney.de/pget.php?&tw=1&pmid=33643764 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33643764 #FOAvip English Wikipedia <ref>{{Cite pmid|33643764}}</ref> Design ensemble deep learning model for pneumonia disease classification #MMPMID33643764 El Asnaoui K Int J Multimed Inf Retr 2021[]; 10 (1): 55-68 PMID33643764show ga With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score). äDesign ensemble deep learning model for pneumonia disease classificati(epmc).pdf DeepDyve Pubget Overpricing