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10.1155/2021/8829829 http://scihub22266oqcxt.onion/10.1155/2021/8829829 33763196!7946481!33763196     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Healthc+Eng 2021 ; 2021 (ä): 8829829 Nephropedia Template TP {{tp|p=33763196|t=2021. Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images |pdf=|usr=}}{{33763196}} gab.com Text Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images JO: J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=33763196 #FOAvip COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models. Twit Text FOAVip Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images ????J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=33763196 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33763196 #FOAvip English Wikipedia <ref>{{Cite pmid|33763196}}</ref> Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images #MMPMID33763196 Kaur M; Kumar V; Yadav V; Singh D; Kumar N; Das NN J Healthc Eng 2021[]; 2021 (ä): 8829829 PMID33763196show ga COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models. |*Deep Learning[MESH] |*Radiography, Thoracic[MESH] |Algorithms[MESH] |COVID-19/*diagnostic imaging[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Neural Networks, Computer[MESH]Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Ima(epmc).pdf DeepDyve Pubget Overpricing