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10.1016/j.chaos.2020.110170 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110170 32834651!7388764!32834651     free     free     free 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 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 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos+Solitons+Fractals 2020 ; 140 (ä): 110170 Nephropedia Template TP {{tp|p=32834651|t=2020. Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches |pdf=|usr=}}{{32834651}} gab.com Text Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834651 #FOAvip COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth. Twit Text FOAVip Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834651 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834651 #FOAvip English Wikipedia <ref>{{Cite pmid|32834651}}</ref> Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches #MMPMID32834651 Hassantabar S; Ahmadi M; Sharifi A Chaos Solitons Fractals 2020[Nov]; 140 (ä): 110170 PMID32834651show ga COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth. äDiagnosis and detection of infected tissue of COVID-19 patients based (epmc).pdf DeepDyve Pubget Overpricing