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10.1007/s00330-021-08050-1 http://scihub22266oqcxt.onion/10.1007/s00330-021-08050-1 34052882!8164481!34052882     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+Radiol 2021 ; 31 (12): 9654-9663 Nephropedia Template TP {{tp|p=34052882|t=2021. COVID-19 classification of X-ray images using deep neural networks |pdf=|usr=}}{{34052882}} gab.com Text COVID-19 classification of X-ray images using deep neural networks JO: Eur Radiol http://www.kidney.de/pget.php?&tw=1&pmid=34052882 #FOAvip OBJECTIVES: In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model's results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals. METHODS: In this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50 ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image. RESULTS: Our model achieved accuracy of 90.3%, (95% CI: 86.3-93.7%) specificity of 90% (95% CI: 84.3-94%), and sensitivity of 90.5% (95% CI: 85-94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93-0.97). CONCLUSION: We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19. KEY POINTS: * A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%. * A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model's image embeddings. Twit Text FOAVip COVID-19 classification of X-ray images using deep neural networks ????Eur Radiol http://www.kidney.de/pget.php?&tw=1&pmid=34052882 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34052882 #FOAvip English Wikipedia <ref>{{Cite pmid|34052882}}</ref> COVID-19 classification of X-ray images using deep neural networks #MMPMID34052882 Keidar D; Yaron D; Goldstein E; Shachar Y; Blass A; Charbinsky L; Aharony I; Lifshitz L; Lumelsky D; Neeman Z; Mizrachi M; Hajouj M; Eizenbach N; Sela E; Weiss CS; Levin P; Benjaminov O; Bachar GN; Tamir S; Rapson Y; Suhami D; Atar E; Dror AA; Bogot NR; Grubstein A; Shabshin N; Elyada YM; Eldar YC Eur Radiol 2021[Dec]; 31 (12): 9654-9663 PMID34052882show ga OBJECTIVES: In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model's results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals. METHODS: In this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50 ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image. RESULTS: Our model achieved accuracy of 90.3%, (95% CI: 86.3-93.7%) specificity of 90% (95% CI: 84.3-94%), and sensitivity of 90.5% (95% CI: 85-94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93-0.97). CONCLUSION: We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19. KEY POINTS: * A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%. * A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model's image embeddings. |*COVID-19[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]COVID-19 classification of X-ray images using deep neural networks (epmc).pdf DeepDyve Pubget Overpricing