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10.2174/1573405617999210112193220 http://scihub22266oqcxt.onion/10.2174/1573405617999210112193220 33438544!ä!33438544 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 2022 ; 18 (6): 587-592 Nephropedia Template TP {{tp|p=33438544|t=2022. An Efficient Method for Coronavirus Detection Through X-rays Using Deep Neural Network |pdf=|usr=}}{{33438544}} gab.com Text An Efficient Method for Coronavirus Detection Through X-rays Using Deep Neural Network JO: Curr Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33438544 #FOAvip BACKGROUND: Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of infection caused by a coronavirus in the respiratory tract can vary from mild to lethal. A serious illness can be developed in old people and those with underlying medical problems like diabetes, cardiovascular disease, cancer, and chronic respiratory disease. For the diagnosis of coronavirus disease, due to the growing number of cases, a limited number of test kits for COVID-19 are available in the hospitals. Hence, it is important to implement an automated system as an immediate alternative diagnostic option to pause the spread of COVID-19 in the population. OBJECTIVE: This paper proposes a deep learning model for the classification of coronavirus infected patient detection using chest X-ray radiographs. METHODS: A fully connected convolutional neural network model is developed to classify healthy and diseased X-ray radiographs. The proposed neural network model consists of seven convolutional layers with the rectified linear unit, softmax (last layer) activation functions, and max-pooling layers which were trained using the publicly available COVID-19 dataset. RESULTS AND CONCLUSION: For validation of the proposed model, the publicly available chest X-ray radiograph dataset consisting of COVID-19 and normal patient's images were used. Considering the performance of the results that are evaluated based on various evaluation metrics such as precision, recall, MSE, RMSE and accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%. Twit Text FOAVip An Efficient Method for Coronavirus Detection Through X-rays Using Deep Neural Network ????Curr Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33438544 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33438544 #FOAvip English Wikipedia <ref>{{Cite pmid|33438544}}</ref> An Efficient Method for Coronavirus Detection Through X-rays Using Deep Neural Network #MMPMID33438544 Rao PS; Bheemavarapu P; Kalyampudi PSL; Rao TVM Curr Med Imaging 2022[]; 18 (6): 587-592 PMID33438544show ga BACKGROUND: Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of infection caused by a coronavirus in the respiratory tract can vary from mild to lethal. A serious illness can be developed in old people and those with underlying medical problems like diabetes, cardiovascular disease, cancer, and chronic respiratory disease. For the diagnosis of coronavirus disease, due to the growing number of cases, a limited number of test kits for COVID-19 are available in the hospitals. Hence, it is important to implement an automated system as an immediate alternative diagnostic option to pause the spread of COVID-19 in the population. OBJECTIVE: This paper proposes a deep learning model for the classification of coronavirus infected patient detection using chest X-ray radiographs. METHODS: A fully connected convolutional neural network model is developed to classify healthy and diseased X-ray radiographs. The proposed neural network model consists of seven convolutional layers with the rectified linear unit, softmax (last layer) activation functions, and max-pooling layers which were trained using the publicly available COVID-19 dataset. RESULTS AND CONCLUSION: For validation of the proposed model, the publicly available chest X-ray radiograph dataset consisting of COVID-19 and normal patient's images were used. Considering the performance of the results that are evaluated based on various evaluation metrics such as precision, recall, MSE, RMSE and accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%. |*COVID-19/diagnostic imaging[MESH] |*Deep Learning[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |Radiography, Thoracic/methods[MESH] |SARS-CoV-2[MESH]An Efficient Method for Coronavirus Detection Through X-rays Using Dee(epmc).pdf DeepDyve Pubget Overpricing