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10.1016/j.mex.2021.101408 http://scihub22266oqcxt.onion/10.1016/j.mex.2021.101408 34109106!8178958!34109106     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 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 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 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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       MethodsX 2021 ; 8 (ä): 101408 Nephropedia Template TP {{tp|p=34109106|t=2021. Truncating a densely connected convolutional neural network with partial layer freezing and feature fusion for diagnosing COVID-19 from chest X-rays |pdf=|usr=}}{{34109106}} gab.com Text Truncating a densely connected convolutional neural network with partial layer freezing and feature fusion for diagnosing COVID-19 from chest X-rays JO: MethodsX http://www.kidney.de/pget.php?&tw=1&pmid=34109106 #FOAvip Deep learning and computer vision revolutionized a new method to automate medical image diagnosis. However, to achieve reliable and state-of-the-art performance, vision-based models require high computing costs and robust datasets. Moreover, even with the conventional training methods, large vision-based models still involve lengthy epochs and costly disk consumptions that can entail difficulty during deployment due to the absence of high-end infrastructures. Therefore, this method modified the training approach on a vision-based model through layer truncation, partial layer freezing, and feature fusion. The proposed method was employed on a Densely Connected Convolutional Neural Network (CNN), the DenseNet model, to diagnose whether a Chest X-Ray (CXR) is well, has Pneumonia, or has COVID-19. From the results, the performance to parameter size ratio highlighted this method's effectiveness to train a DenseNet model with fewer parameters compared to traditionally trained state-of-the-art Deep CNN (DCNN) models, yet yield promising results.*This novel method significantly reduced the model's parameter size without sacrificing much of its classification performance.*The proposed method had better performance against some state-of-the-art Deep Convolutional Neural Network (DCNN) models that diagnosed samples of CXRs with COVID-19.*The proposed method delivered a conveniently scalable, reproducible, and deployable DCNN model for most low-end devices. Twit Text FOAVip Truncating a densely connected convolutional neural network with partial layer freezing and feature fusion for diagnosing COVID-19 from chest X-rays ????MethodsX http://www.kidney.de/pget.php?&tw=1&pmid=34109106 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34109106 #FOAvip English Wikipedia <ref>{{Cite pmid|34109106}}</ref> Truncating a densely connected convolutional neural network with partial layer freezing and feature fusion for diagnosing COVID-19 from chest X-rays #MMPMID34109106 Montalbo FJP MethodsX 2021[]; 8 (ä): 101408 PMID34109106show ga Deep learning and computer vision revolutionized a new method to automate medical image diagnosis. However, to achieve reliable and state-of-the-art performance, vision-based models require high computing costs and robust datasets. Moreover, even with the conventional training methods, large vision-based models still involve lengthy epochs and costly disk consumptions that can entail difficulty during deployment due to the absence of high-end infrastructures. Therefore, this method modified the training approach on a vision-based model through layer truncation, partial layer freezing, and feature fusion. The proposed method was employed on a Densely Connected Convolutional Neural Network (CNN), the DenseNet model, to diagnose whether a Chest X-Ray (CXR) is well, has Pneumonia, or has COVID-19. From the results, the performance to parameter size ratio highlighted this method's effectiveness to train a DenseNet model with fewer parameters compared to traditionally trained state-of-the-art Deep CNN (DCNN) models, yet yield promising results.*This novel method significantly reduced the model's parameter size without sacrificing much of its classification performance.*The proposed method had better performance against some state-of-the-art Deep Convolutional Neural Network (DCNN) models that diagnosed samples of CXRs with COVID-19.*The proposed method delivered a conveniently scalable, reproducible, and deployable DCNN model for most low-end devices. äTruncating a densely connected convolutional neural network with parti(epmc).pdf DeepDyve Pubget Overpricing