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10.1016/j.ijleo.2021.167572 http://scihub22266oqcxt.onion/10.1016/j.ijleo.2021.167572 34248209!8260491!34248209     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Optik+(Stuttg) 2021 ; 244 (ä): 167572 Nephropedia Template TP {{tp|p=34248209|t=2021. An optimized KELM approach for the diagnosis of COVID-19 from 2D-SSA reconstructed CXR Images |pdf=|usr=}}{{34248209}} gab.com Text An optimized KELM approach for the diagnosis of COVID-19 from 2D-SSA reconstructed CXR Images JO: Optik (Stuttg) http://www.kidney.de/pget.php?&tw=1&pmid=34248209 #FOAvip The COVID-19 is proved to be the most infectious disease of the current century with a high mortality rate world-wide. The current RT-PCR test standard for the diagnosis of COVID-19 is an invasive and time-consuming procedure, whereas the chest X-ray (CXR) images provide a non-invasive and time/cost-effective method for COVID-19 diagnosis. The current existing deep learning methods for the detection and diagnosis of CXR images provide biased results for the small size dataset available. Hence, in the present work, a conventional yet efficient method is proposed classifying the CXR images into COVID-19, Pneumonia, and Normal. The proposed approach pre-processes the CXR images using 2D singular spectrum analysis (SSA) for image reconstruction which enhances the feature inputs to the classifier. The features are extracted from the reconstructed images using a block-based GLCM approach. Then, a grasshopper-based Kernel extreme learning machine (KELM) is proposed which finds the optimal features and kernel parameters of KELM at the same instance. From the experimental analysis, it is seen that the present work outperforms that of other competent schemes in terms of classification accuracy with a minimal set of features extracted from the first 2 eigen components of the 2D-SSA reconstructed image with 5 x 5 decomposition. Twit Text FOAVip An optimized KELM approach for the diagnosis of COVID-19 from 2D-SSA reconstructed CXR Images ????Optik (Stuttg) http://www.kidney.de/pget.php?&tw=1&pmid=34248209 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34248209 #FOAvip English Wikipedia <ref>{{Cite pmid|34248209}}</ref> An optimized KELM approach for the diagnosis of COVID-19 from 2D-SSA reconstructed CXR Images #MMPMID34248209 Mohanty F; Dora C Optik (Stuttg) 2021[Oct]; 244 (ä): 167572 PMID34248209show ga The COVID-19 is proved to be the most infectious disease of the current century with a high mortality rate world-wide. The current RT-PCR test standard for the diagnosis of COVID-19 is an invasive and time-consuming procedure, whereas the chest X-ray (CXR) images provide a non-invasive and time/cost-effective method for COVID-19 diagnosis. The current existing deep learning methods for the detection and diagnosis of CXR images provide biased results for the small size dataset available. Hence, in the present work, a conventional yet efficient method is proposed classifying the CXR images into COVID-19, Pneumonia, and Normal. The proposed approach pre-processes the CXR images using 2D singular spectrum analysis (SSA) for image reconstruction which enhances the feature inputs to the classifier. The features are extracted from the reconstructed images using a block-based GLCM approach. Then, a grasshopper-based Kernel extreme learning machine (KELM) is proposed which finds the optimal features and kernel parameters of KELM at the same instance. From the experimental analysis, it is seen that the present work outperforms that of other competent schemes in terms of classification accuracy with a minimal set of features extracted from the first 2 eigen components of the 2D-SSA reconstructed image with 5 x 5 decomposition. äAn optimized KELM approach for the diagnosis of COVID-19 from 2D-SSA (epmc).pdf DeepDyve Pubget Overpricing