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10.1109/TUFFC.2020.3005512 http://scihub22266oqcxt.onion/10.1109/TUFFC.2020.3005512 32746195!8544930!32746195     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Ultrason+Ferroelectr+Freq+Control 2020 ; 67 (11): 2207-2217 Nephropedia Template TP {{tp|p=32746195|t=2020. Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data |pdf=|usr=}}{{32746195}} gab.com Text Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data JO: IEEE Trans Ultrason Ferroelectr Freq Control http://www.kidney.de/pget.php?&tw=1&pmid=32746195 #FOAvip Recent works highlighted the significant potential of lung ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID-19 diagnostic. In this article, we propose an automatic and unsupervised method for the detection and localization of the pleural line in LUS data based on the hidden Markov model and Viterbi Algorithm. The pleural line localization step is followed by a supervised classification procedure based on the support vector machine (SVM). The classifier evaluates the healthiness level of a patient and, if present, the severity of the pathology, i.e., the score value for each image of a given LUS acquisition. The experiments performed on a variety of LUS data acquired in Italian hospitals with both linear and convex probes highlight the effectiveness of the proposed method. The average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. The accuracy of the SVM classification in correctly evaluating the severity of COVID-19 related pleural line alterations is about 88% and 94% for convex and linear probes, respectively. The results as well as the visualization of the detected pleural line and the predicted score chart, provide a significant support to medical staff for further evaluating the patient condition. Twit Text FOAVip Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data ????IEEE Trans Ultrason Ferroelectr Freq Control http://www.kidney.de/pget.php?&tw=1&pmid=32746195 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32746195 #FOAvip English Wikipedia <ref>{{Cite pmid|32746195}}</ref> Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data #MMPMID32746195 Carrer L; Donini E; Marinelli D; Zanetti M; Mento F; Torri E; Smargiassi A; Inchingolo R; Soldati G; Demi L; Bovolo F; Bruzzone L IEEE Trans Ultrason Ferroelectr Freq Control 2020[Nov]; 67 (11): 2207-2217 PMID32746195show ga Recent works highlighted the significant potential of lung ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID-19 diagnostic. In this article, we propose an automatic and unsupervised method for the detection and localization of the pleural line in LUS data based on the hidden Markov model and Viterbi Algorithm. The pleural line localization step is followed by a supervised classification procedure based on the support vector machine (SVM). The classifier evaluates the healthiness level of a patient and, if present, the severity of the pathology, i.e., the score value for each image of a given LUS acquisition. The experiments performed on a variety of LUS data acquired in Italian hospitals with both linear and convex probes highlight the effectiveness of the proposed method. The average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. The accuracy of the SVM classification in correctly evaluating the severity of COVID-19 related pleural line alterations is about 88% and 94% for convex and linear probes, respectively. The results as well as the visualization of the detected pleural line and the predicted score chart, provide a significant support to medical staff for further evaluating the patient condition. |Algorithms[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Lung/*diagnostic imaging[MESH] |Pandemics[MESH] |Pleura/*diagnostic imaging[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Signal Processing, Computer-Assisted[MESH] |Support Vector Machine[MESH]Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultra(epmc).pdf DeepDyve Pubget Overpricing