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.

10.1259/bjro.20200028 http://scihub22266oqcxt.onion/10.1259/bjro.20200028 33718765!7931407!33718765     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BJR+Open 2021 ; 3 (1): 20200028 Nephropedia Template TP {{tp|p=33718765|t=2021. Run length encoding based wavelet features for COVID-19 detection in X-rays |pdf=|usr=}}{{33718765}} gab.com Text Run length encoding based wavelet features for COVID-19 detection in X-rays JO: BJR Open http://www.kidney.de/pget.php?&tw=1&pmid=33718765 #FOAvip OBJECTIVES: Introduced in his paper is a novel approach for the recognition of COVID-19 cases in chest X-rays. METHODS: The discrete Wavelet transform (DWT) is employed in the proposed system to obtain highly discriminative features from the input chest X-ray image. The selected features are then classified by a support vector machine (SVM) classifier as either normal or COVID-19 cases. The DWT is well-known for its energy compression power. The proposed system uses the DWT to decompose the chest X-ray image into a group of approximation coefficients that contain a small number of high-energy (high-magnitude) coefficients. The proposed system introduces a novel coefficient selection scheme that employs hard thresholding combined with run-length encoding to extract only high-magnitude Wavelet approximation coefficients. These coefficients are utilized as features symbolizing the chest X-ray input image. After applying zero-padding to unify their lengths, the feature vectors are introduced to a SVM which classifies them as either normal or COVID-19 cases. RESULTS: The proposed system yields promising results in terms of classification accuracy, which justifies further work in this direction. CONCLUSION: The DWT can produce a few features that are highly discriminative. By reducing the dimensionality of the feature space, the proposed system is able to reduce the number of required training images and diminish the space and time complexities of the system. ADVANCES IN KNOWLEDGE: Exploiting and reshaping the approximation coefficients can produce discriminative features representing the input image. Twit Text FOAVip Run length encoding based wavelet features for COVID-19 detection in X-rays ????BJR Open http://www.kidney.de/pget.php?&tw=1&pmid=33718765 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33718765 #FOAvip English Wikipedia <ref>{{Cite pmid|33718765}}</ref> Run length encoding based wavelet features for COVID-19 detection in X-rays #MMPMID33718765 Sarhan A BJR Open 2021[]; 3 (1): 20200028 PMID33718765show ga OBJECTIVES: Introduced in his paper is a novel approach for the recognition of COVID-19 cases in chest X-rays. METHODS: The discrete Wavelet transform (DWT) is employed in the proposed system to obtain highly discriminative features from the input chest X-ray image. The selected features are then classified by a support vector machine (SVM) classifier as either normal or COVID-19 cases. The DWT is well-known for its energy compression power. The proposed system uses the DWT to decompose the chest X-ray image into a group of approximation coefficients that contain a small number of high-energy (high-magnitude) coefficients. The proposed system introduces a novel coefficient selection scheme that employs hard thresholding combined with run-length encoding to extract only high-magnitude Wavelet approximation coefficients. These coefficients are utilized as features symbolizing the chest X-ray input image. After applying zero-padding to unify their lengths, the feature vectors are introduced to a SVM which classifies them as either normal or COVID-19 cases. RESULTS: The proposed system yields promising results in terms of classification accuracy, which justifies further work in this direction. CONCLUSION: The DWT can produce a few features that are highly discriminative. By reducing the dimensionality of the feature space, the proposed system is able to reduce the number of required training images and diminish the space and time complexities of the system. ADVANCES IN KNOWLEDGE: Exploiting and reshaping the approximation coefficients can produce discriminative features representing the input image. äRun length encoding based wavelet features for COVID-19 detection in X(epmc).pdf DeepDyve Pubget Overpricing