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10.1007/s00530-021-00794-6 http://scihub22266oqcxt.onion/10.1007/s00530-021-00794-6 33935377!8079233!33935377     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       Multimed+Syst 2023 ; 29 (3): 1729-1738 Nephropedia Template TP {{tp|p=33935377|t=2023. Medical image-based detection of COVID-19 using Deep Convolution Neural Networks |pdf=|usr=}}{{33935377}} gab.com Text Medical image-based detection of COVID-19 using Deep Convolution Neural Networks JO: Multimed Syst http://www.kidney.de/pget.php?&tw=1&pmid=33935377 #FOAvip The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures. Twit Text FOAVip Medical image-based detection of COVID-19 using Deep Convolution Neural Networks ????Multimed Syst http://www.kidney.de/pget.php?&tw=1&pmid=33935377 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33935377 #FOAvip English Wikipedia <ref>{{Cite pmid|33935377}}</ref> Medical image-based detection of COVID-19 using Deep Convolution Neural Networks #MMPMID33935377 Gaur L; Bhatia U; Jhanjhi NZ; Muhammad G; Masud M Multimed Syst 2023[]; 29 (3): 1729-1738 PMID33935377show ga The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures. äMedical image-based detection of COVID-19 using Deep Convolution Neura(epmc).pdf DeepDyve Pubget Overpricing