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10.1007/s00521-020-05410-8 http://scihub22266oqcxt.onion/10.1007/s00521-020-05410-8 33487885!7814271!33487885     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Neural+Comput+Appl 2022 ; 34 (14): 11423-11440 Nephropedia Template TP {{tp|p=33487885|t=2022. Efficient deep learning approach for augmented detection of Coronavirus disease |pdf=|usr=}}{{33487885}} gab.com Text Efficient deep learning approach for augmented detection of Coronavirus disease JO: Neural Comput Appl http://www.kidney.de/pget.php?&tw=1&pmid=33487885 #FOAvip The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening. Twit Text FOAVip Efficient deep learning approach for augmented detection of Coronavirus disease ????Neural Comput Appl http://www.kidney.de/pget.php?&tw=1&pmid=33487885 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33487885 #FOAvip English Wikipedia <ref>{{Cite pmid|33487885}}</ref> Efficient deep learning approach for augmented detection of Coronavirus disease #MMPMID33487885 Sedik A; Hammad M; Abd El-Samie FE; Gupta BB; Abd El-Latif AA Neural Comput Appl 2022[]; 34 (14): 11423-11440 PMID33487885show ga The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening. äEfficient deep learning approach for augmented detection of Coronaviru(epmc).pdf DeepDyve Pubget Overpricing