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10.3390/s21062174 http://scihub22266oqcxt.onion/10.3390/s21062174 33804609!8003645!33804609     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 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       Sensors+(Basel) 2021 ; 21 (6): ä Nephropedia Template TP {{tp|p=33804609|t=2021. Hyperparameter Optimization for COVID-19 Pneumonia Diagnosis Based on Chest CT |pdf=|usr=}}{{33804609}} gab.com Text Hyperparameter Optimization for COVID-19 Pneumonia Diagnosis Based on Chest CT JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33804609 #FOAvip Convolutional Neural Networks (CNNs) have been successfully applied in the medical diagnosis of different types of diseases. However, selecting the architecture and the best set of hyperparameters among the possible combinations can be a significant challenge. The purpose of this work is to investigate the use of the Hyperband optimization algorithm in the process of optimizing a CNN applied to the diagnosis of SARS-Cov2 disease (COVID-19). The test was performed with the Optuna framework, and the optimization process aimed to optimize four hyperparameters: (1) backbone architecture, (2) the number of inception modules, (3) the number of neurons in the fully connected layers, and (4) the learning rate. CNNs were trained on 2175 computed tomography (CT) images. The CNN that was proposed by the optimization process was a VGG16 with five inception modules, 128 neurons in the two fully connected layers, and a learning rate of 0.0027. The proposed method achieved a sensitivity, precision, and accuracy of 97%, 82%, and 88%, outperforming the sensitivity of the Real-Time Polymerase Chain Reaction (RT-PCR) tests (53-88%) and the accuracy of the diagnosis performed by human experts (72%). Twit Text FOAVip Hyperparameter Optimization for COVID-19 Pneumonia Diagnosis Based on Chest CT ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33804609 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33804609 #FOAvip English Wikipedia <ref>{{Cite pmid|33804609}}</ref> Hyperparameter Optimization for COVID-19 Pneumonia Diagnosis Based on Chest CT #MMPMID33804609 Lacerda P; Barros B; Albuquerque C; Conci A Sensors (Basel) 2021[Mar]; 21 (6): ä PMID33804609show ga Convolutional Neural Networks (CNNs) have been successfully applied in the medical diagnosis of different types of diseases. However, selecting the architecture and the best set of hyperparameters among the possible combinations can be a significant challenge. The purpose of this work is to investigate the use of the Hyperband optimization algorithm in the process of optimizing a CNN applied to the diagnosis of SARS-Cov2 disease (COVID-19). The test was performed with the Optuna framework, and the optimization process aimed to optimize four hyperparameters: (1) backbone architecture, (2) the number of inception modules, (3) the number of neurons in the fully connected layers, and (4) the learning rate. CNNs were trained on 2175 computed tomography (CT) images. The CNN that was proposed by the optimization process was a VGG16 with five inception modules, 128 neurons in the two fully connected layers, and a learning rate of 0.0027. The proposed method achieved a sensitivity, precision, and accuracy of 97%, 82%, and 88%, outperforming the sensitivity of the Real-Time Polymerase Chain Reaction (RT-PCR) tests (53-88%) and the accuracy of the diagnosis performed by human experts (72%). |*Deep Learning[MESH] |*Diagnosis, Computer-Assisted[MESH] |*Neural Networks, Computer[MESH] |*Tomography, X-Ray Computed[MESH] |COVID-19/*diagnosis[MESH]Hyperparameter Optimization for COVID-19 Pneumonia Diagnosis Based on (epmc).pdf DeepDyve Pubget Overpricing