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10.1007/s10044-020-00950-0 http://scihub22266oqcxt.onion/10.1007/s10044-020-00950-0 33500681!7819829!33500681     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pattern+Anal+Appl 2021 ; 24 (3): 951-964 Nephropedia Template TP {{tp|p=33500681|t=2021. A novel framework for rapid diagnosis of COVID-19 on computed tomography scans |pdf=|usr=}}{{33500681}} gab.com Text A novel framework for rapid diagnosis of COVID-19 on computed tomography scans JO: Pattern Anal Appl http://www.kidney.de/pget.php?&tw=1&pmid=33500681 #FOAvip Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept. Twit Text FOAVip A novel framework for rapid diagnosis of COVID-19 on computed tomography scans ????Pattern Anal Appl http://www.kidney.de/pget.php?&tw=1&pmid=33500681 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33500681 #FOAvip English Wikipedia <ref>{{Cite pmid|33500681}}</ref> A novel framework for rapid diagnosis of COVID-19 on computed tomography scans #MMPMID33500681 Akram T; Attique M; Gul S; Shahzad A; Altaf M; Naqvi SSR; Damasevicius R; Maskeliunas R Pattern Anal Appl 2021[]; 24 (3): 951-964 PMID33500681show ga Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept. äA novel framework for rapid diagnosis of COVID-19 on computed tomograp(epmc).pdf DeepDyve Pubget Overpricing