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10.7507/1001-5515.202005056 http://scihub22266oqcxt.onion/10.7507/1001-5515.202005056 32840070!10319551!32840070     free     free     free Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538 Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538       Sheng+Wu+Yi+Xue+Gong+Cheng+Xue+Za+Zhi 2020 ; 37 (4): 557-565 Nephropedia Template TP {{tp|p=32840070|t=2020. Research on coronavirus disease 2019 (COVID-19) detection method based on depthwise separable DenseNet in chest X-ray images |pdf=|usr=}}{{32840070}} gab.com Text Research on coronavirus disease 2019 (COVID-19) detection method based on depthwise separable DenseNet in chest X-ray images JO: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=32840070 #FOAvip Coronavirus disease 2019 (COVID-19) has spread rapidly around the world. In order to diagnose COVID-19 more quickly, in this paper, a depthwise separable DenseNet was proposed. The paper constructed a deep learning model with 2 905 chest X-ray images as experimental dataset. In order to enhance the contrast, the contrast limited adaptive histogram equalization (CLAHE) algorithm was used to preprocess the X-ray image before network training, then the images were put into the training network and the parameters of the network were adjusted to the optimal. Meanwhile, Leaky ReLU was selected as the activation function. VGG16, ResNet18, ResNet34, DenseNet121 and SDenseNet models were used to compare with the model proposed in this paper. Compared with ResNet34, the proposed classification model of pneumonia had improved 2.0%, 2.3% and 1.5% in accuracy, sensitivity and specificity respectively. Compared with the SDenseNet network without depthwise separable convolution, number of parameters of the proposed model was reduced by 43.9%, but the classification effect did not decrease. It can be found that the proposed DWSDenseNet has a good classification effect on the COVID-19 chest X-ray images dataset. Under the condition of ensuring the accuracy as much as possible, the depthwise separable convolution can effectively reduce number of parameters of the model. Twit Text FOAVip Research on coronavirus disease 2019 (COVID-19) detection method based on depthwise separable DenseNet in chest X-ray images ????Sheng Wu Yi Xue Gong Cheng Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=32840070 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32840070 #FOAvip English Wikipedia <ref>{{Cite pmid|32840070}}</ref> Research on coronavirus disease 2019 (COVID-19) detection method based on depthwise separable DenseNet in chest X-ray images #MMPMID32840070 Feng Y; Qiu D; Cao H; Zhang J; Xin Z; Liu J Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2020[Aug]; 37 (4): 557-565 PMID32840070show ga Coronavirus disease 2019 (COVID-19) has spread rapidly around the world. In order to diagnose COVID-19 more quickly, in this paper, a depthwise separable DenseNet was proposed. The paper constructed a deep learning model with 2 905 chest X-ray images as experimental dataset. In order to enhance the contrast, the contrast limited adaptive histogram equalization (CLAHE) algorithm was used to preprocess the X-ray image before network training, then the images were put into the training network and the parameters of the network were adjusted to the optimal. Meanwhile, Leaky ReLU was selected as the activation function. VGG16, ResNet18, ResNet34, DenseNet121 and SDenseNet models were used to compare with the model proposed in this paper. Compared with ResNet34, the proposed classification model of pneumonia had improved 2.0%, 2.3% and 1.5% in accuracy, sensitivity and specificity respectively. Compared with the SDenseNet network without depthwise separable convolution, number of parameters of the proposed model was reduced by 43.9%, but the classification effect did not decrease. It can be found that the proposed DWSDenseNet has a good classification effect on the COVID-19 chest X-ray images dataset. Under the condition of ensuring the accuracy as much as possible, the depthwise separable convolution can effectively reduce number of parameters of the model. |*Betacoronavirus[MESH] |*Coronavirus Infections/diagnostic imaging[MESH] |*Pandemics[MESH] |*Pneumonia, Viral/diagnostic imaging[MESH] |COVID-19[MESH] |Deep Learning[MESH] |Humans[MESH] |SARS-CoV-2[MESH] Research on coronavirus disease 2019 (COVID-19) detection method base(epmc).pdf DeepDyve Pubget Overpricing