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10.2174/1573405616666201123120417 http://scihub22266oqcxt.onion/10.2174/1573405616666201123120417 33231160!8653418!33231160     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33231160&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Curr+Med+Imaging 2021 ; 17 (8): 973-980 Nephropedia Template TP {{tp|p=33231160|t=2021. Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems |pdf=|usr=}}{{33231160}} gab.com Text Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems JO: Curr Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33231160 #FOAvip OBJECTIVE: Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images. METHODS: This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community. RESULTS: Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation. CONCLUSION: A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19. Twit Text FOAVip Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems ????Curr Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33231160 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33231160 #FOAvip English Wikipedia <ref>{{Cite pmid|33231160}}</ref> Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems #MMPMID33231160 Albahli S; Albattah W Curr Med Imaging 2021[]; 17 (8): 973-980 PMID33231160show ga OBJECTIVE: Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images. METHODS: This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community. RESULTS: Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation. CONCLUSION: A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19. |*COVID-19/diagnosis/epidemiology[MESH] |*Deep Learning[MESH] |*Neural Networks, Computer[MESH] |Humans[MESH] |Radiography, Thoracic[MESH]Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited(epmc).pdf DeepDyve Pubget Overpricing