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10.1155/2020/8889412 http://scihub22266oqcxt.onion/10.1155/2020/8889412 33299538!7684157!33299538     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 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 2020 ; 2020 (ä): 8889412 Nephropedia Template TP {{tp|p=33299538|t=2020. Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients |pdf=|usr=}}{{33299538}} gab.com Text Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients JO: J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=33299538 #FOAvip Due to the rapid spread of COVID-19 and its induced death worldwide, it is imperative to develop a reliable tool for the early detection of this disease. Chest X-ray is currently accepted to be one of the reliable means for such a detection purpose. However, most of the available methods utilize large training data, and there is a need for improvement in the detection accuracy due to the limited boundary segment of the acquired images for symptom identifications. In this study, a robust and efficient method based on transfer learning techniques is proposed to identify normal and COVID-19 patients by employing small training data. Transfer learning builds accurate models in a timesaving way. First, data augmentation was performed to help the network for memorization of image details. Next, five state-of-the-art transfer learning models, AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, and Xception, with three optimizers, Adam, SGDM, and RMSProp, were implemented at various learning rates, 1e-4, 2e-4, 3e-4, and 4e-4, to reduce the probability of overfitting. All the experiments were performed on publicly available datasets with several analytical measurements attained after execution with a 10-fold cross-validation method. The results suggest that MobileNetv2 with Adam optimizer at a learning rate of 3e-4 provides an average accuracy, recall, precision, and F-score of 97%, 96.5%, 97.5%, and 97%, respectively, which are higher than those of all other combinations. The proposed method is competitive with the available literature, demonstrating that it could be used for the early detection of COVID-19 patients. Twit Text FOAVip Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients ????J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=33299538 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33299538 #FOAvip English Wikipedia <ref>{{Cite pmid|33299538}}</ref> Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients #MMPMID33299538 Fan Z; Jamil M; Sadiq MT; Huang X; Yu X J Healthc Eng 2020[]; 2020 (ä): 8889412 PMID33299538show ga Due to the rapid spread of COVID-19 and its induced death worldwide, it is imperative to develop a reliable tool for the early detection of this disease. Chest X-ray is currently accepted to be one of the reliable means for such a detection purpose. However, most of the available methods utilize large training data, and there is a need for improvement in the detection accuracy due to the limited boundary segment of the acquired images for symptom identifications. In this study, a robust and efficient method based on transfer learning techniques is proposed to identify normal and COVID-19 patients by employing small training data. Transfer learning builds accurate models in a timesaving way. First, data augmentation was performed to help the network for memorization of image details. Next, five state-of-the-art transfer learning models, AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, and Xception, with three optimizers, Adam, SGDM, and RMSProp, were implemented at various learning rates, 1e-4, 2e-4, 3e-4, and 4e-4, to reduce the probability of overfitting. All the experiments were performed on publicly available datasets with several analytical measurements attained after execution with a 10-fold cross-validation method. The results suggest that MobileNetv2 with Adam optimizer at a learning rate of 3e-4 provides an average accuracy, recall, precision, and F-score of 97%, 96.5%, 97.5%, and 97%, respectively, which are higher than those of all other combinations. The proposed method is competitive with the available literature, demonstrating that it could be used for the early detection of COVID-19 patients. |*Machine Learning[MESH] |COVID-19/*diagnostic imaging[MESH] |Databases, Factual[MESH] |Early Diagnosis[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH] |Radiography, Thoracic[MESH] |Reproducibility of Results[MESH] |SARS-CoV-2[MESH] |Sensitivity and Specificity[MESH]Exploiting Multiple Optimizers with Transfer Learning Techniques for t(epmc).pdf DeepDyve Pubget Overpricing