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10.2174/1573405616666200604163954 http://scihub22266oqcxt.onion/10.2174/1573405616666200604163954 32496988!ä!32496988 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Curr+Med+Imaging 2021 ; 17 (1): 109-119 Nephropedia Template TP {{tp|p=32496988|t=2021. A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images |pdf=|usr=}}{{32496988}} gab.com Text A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images JO: Curr Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32496988 #FOAvip BACKGROUND: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at pathological x-ray images. Most of the works only target single disease detection which is not good enough. Some works have been provided for all classes. However, the results suffer due to lack of data for rare classes and data unbalancing problem. METHODS: Due to the rise in COVID-19 cases, medical facilities in many countries are overwhelmed and there is a need for an intelligent system to detect it. Few works have been done regarding the detection of the coronavirus but there are many cases where it can be misclassified as some techniques are not efficient and can only identify specific diseases. This work is a deep learning- based model to distinguish COVID-19 cases from other chest diseases. RESULTS: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides an effective analysis of chest-related diseases taking into account both age and gender. Our model achieves 87% accuracy in terms of GAN-based synthetic data and presents four different types of deep learning-based models that provide comparable results to other state-of-the-art techniques. CONCLUSION: The healthcare industry may face unfavorable consequences if the gap in the identification of all types of pneumonia is not filled with effective automation. Twit Text FOAVip A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images ????Curr Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32496988 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32496988 #FOAvip English Wikipedia <ref>{{Cite pmid|32496988}}</ref> A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images #MMPMID32496988 Albahli S Curr Med Imaging 2021[]; 17 (1): 109-119 PMID32496988show ga BACKGROUND: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at pathological x-ray images. Most of the works only target single disease detection which is not good enough. Some works have been provided for all classes. However, the results suffer due to lack of data for rare classes and data unbalancing problem. METHODS: Due to the rise in COVID-19 cases, medical facilities in many countries are overwhelmed and there is a need for an intelligent system to detect it. Few works have been done regarding the detection of the coronavirus but there are many cases where it can be misclassified as some techniques are not efficient and can only identify specific diseases. This work is a deep learning- based model to distinguish COVID-19 cases from other chest diseases. RESULTS: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides an effective analysis of chest-related diseases taking into account both age and gender. Our model achieves 87% accuracy in terms of GAN-based synthetic data and presents four different types of deep learning-based models that provide comparable results to other state-of-the-art techniques. CONCLUSION: The healthcare industry may face unfavorable consequences if the gap in the identification of all types of pneumonia is not filled with effective automation. |*Neural Networks, Computer[MESH] |Adolescent[MESH] |Adult[MESH] |Aged[MESH] |COVID-19/*diagnostic imaging[MESH] |Child[MESH] |Child, Preschool[MESH] |Diagnosis, Differential[MESH] |Female[MESH] |Humans[MESH] |Infant[MESH] |Male[MESH] |Middle Aged[MESH] |Radiography, Thoracic/methods[MESH] |Thoracic Diseases/*diagnostic imaging[MESH]A Deep Neural Network to Distinguish COVID-19 from other Chest Disease(epmc).pdf DeepDyve Pubget Overpricing