Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1155/2021/5544742 http://scihub22266oqcxt.onion/10.1155/2021/5544742 33954175!8054863!33954175     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2021 ; 2021 (ä): 5544742 Nephropedia Template TP {{tp|p=33954175|t=2021. Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images |pdf=|usr=}}{{33954175}} gab.com Text Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=33954175 #FOAvip The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c-means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 +/- 0.3 mm, and volume overlap error (VOE) of 5.6 +/- 1.2%. Twit Text FOAVip Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=33954175 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33954175 #FOAvip English Wikipedia <ref>{{Cite pmid|33954175}}</ref> Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images #MMPMID33954175 Ranjbarzadeh R; Jafarzadeh Ghoushchi S; Bendechache M; Amirabadi A; Ab Rahman MN; Baseri Saadi S; Aghamohammadi A; Kooshki Forooshani M Biomed Res Int 2021[]; 2021 (ä): 5544742 PMID33954175show ga The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c-means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 +/- 0.3 mm, and volume overlap error (VOE) of 5.6 +/- 1.2%. |*Algorithms[MESH] |*Pandemics[MESH] |COVID-19/*diagnostic imaging/epidemiology/virology[MESH] |Cluster Analysis[MESH] |Deep Learning[MESH] |Female[MESH] |Humans[MESH] |Lung/diagnostic imaging/virology[MESH] |Male[MESH] |Neural Networks, Computer[MESH] |Pneumonia, Viral/*diagnostic imaging/virology[MESH] |Radiographic Image Interpretation, Computer-Assisted[MESH] |SARS-CoV-2/*physiology[MESH]Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade (epmc).pdf DeepDyve Pubget Overpricing