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10.1109/TNNLS.2021.3070467 http://scihub22266oqcxt.onion/10.1109/TNNLS.2021.3070467 33872157!8544941!33872157     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 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Neural+Netw+Learn+Syst 2021 ; 32 (5): 1810-1820 Nephropedia Template TP {{tp|p=33872157|t=2021. Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images |pdf=|usr=}}{{33872157}} gab.com Text Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images JO: IEEE Trans Neural Netw Learn Syst http://www.kidney.de/pget.php?&tw=1&pmid=33872157 #FOAvip Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy. Twit Text FOAVip Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images ????IEEE Trans Neural Netw Learn Syst http://www.kidney.de/pget.php?&tw=1&pmid=33872157 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33872157 #FOAvip English Wikipedia <ref>{{Cite pmid|33872157}}</ref> Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images #MMPMID33872157 Yamac M; Ahishali M; Degerli A; Kiranyaz S; Chowdhury MEH; Gabbouj M IEEE Trans Neural Netw Learn Syst 2021[May]; 32 (5): 1810-1820 PMID33872157show ga Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy. |*Deep Learning/classification[MESH] |*Neural Networks, Computer[MESH] |*X-Rays[MESH] |COVID-19/classification/*diagnostic imaging[MESH] |Diagnosis, Differential[MESH] |Humans[MESH] |Pneumonia, Bacterial/classification/diagnostic imaging[MESH] |Pneumonia, Viral/classification/diagnostic imaging[MESH]Convolutional Sparse Support Estimator-Based COVID-19 Recognition From(epmc).pdf DeepDyve Pubget Overpricing