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10.1155/2021/8854892 http://scihub22266oqcxt.onion/10.1155/2021/8854892 33968160!8081632!33968160     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Math+Methods+Med 2021 ; 2021 (ä): 8854892 Nephropedia Template TP {{tp|p=33968160|t=2021. Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN |pdf=|usr=}}{{33968160}} gab.com Text Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN JO: Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=33968160 #FOAvip Pneumonia remains a threat to human health; the coronavirus disease 2019 (COVID-19) that began at the end of 2019 had a major impact on the world. It is still raging in many countries and has caused great losses to people's lives and property. In this paper, we present a method based on DeepConv-DilatedNet of identifying and localizing pneumonia in chest X-ray (CXR) images. Two-stage detector Faster R-CNN is adopted as the structure of a network. Feature Pyramid Network (FPN) is integrated into the residual neural network of a dilated bottleneck so that the deep features are expanded to preserve the deep feature and position information of the object. In the case of DeepConv-DilatedNet, the deconvolution network is used to restore high-level feature maps into its original size, and the target information is further retained. On the other hand, DeepConv-DilatedNet uses a popular fully convolution architecture with computation shared on the entire image. Then, Soft-NMS is used to screen boxes and ensure sample quality. Also, K-Means++ is used to generate anchor boxes to improve the localization accuracy. The algorithm obtained 39.23% Mean Average Precision (mAP) on the X-ray image dataset from the Radiological Society of North America (RSNA) and got 38.02% Mean Average Precision (mAP) on the ChestX-ray14 dataset, surpassing other detection algorithms. So, in this paper, an improved algorithm that can provide doctors with location information of pneumonia lesions is proposed. Twit Text FOAVip Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN ????Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=33968160 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33968160 #FOAvip English Wikipedia <ref>{{Cite pmid|33968160}}</ref> Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN #MMPMID33968160 Yao S; Chen Y; Tian X; Jiang R Comput Math Methods Med 2021[]; 2021 (ä): 8854892 PMID33968160show ga Pneumonia remains a threat to human health; the coronavirus disease 2019 (COVID-19) that began at the end of 2019 had a major impact on the world. It is still raging in many countries and has caused great losses to people's lives and property. In this paper, we present a method based on DeepConv-DilatedNet of identifying and localizing pneumonia in chest X-ray (CXR) images. Two-stage detector Faster R-CNN is adopted as the structure of a network. Feature Pyramid Network (FPN) is integrated into the residual neural network of a dilated bottleneck so that the deep features are expanded to preserve the deep feature and position information of the object. In the case of DeepConv-DilatedNet, the deconvolution network is used to restore high-level feature maps into its original size, and the target information is further retained. On the other hand, DeepConv-DilatedNet uses a popular fully convolution architecture with computation shared on the entire image. Then, Soft-NMS is used to screen boxes and ensure sample quality. Also, K-Means++ is used to generate anchor boxes to improve the localization accuracy. The algorithm obtained 39.23% Mean Average Precision (mAP) on the X-ray image dataset from the Radiological Society of North America (RSNA) and got 38.02% Mean Average Precision (mAP) on the ChestX-ray14 dataset, surpassing other detection algorithms. So, in this paper, an improved algorithm that can provide doctors with location information of pneumonia lesions is proposed. |*Pattern Recognition, Automated[MESH] |Algorithms[MESH] |COVID-19/*complications/*diagnostic imaging[MESH] |Deep Learning[MESH] |Diagnosis, Computer-Assisted[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |Neural Networks, Computer[MESH] |Pneumonia/*diagnostic imaging[MESH] |ROC Curve[MESH] |Radiography, Thoracic[MESH]Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN (epmc).pdf DeepDyve Pubget Overpricing