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10.1007/s12539-020-00403-6 http://scihub22266oqcxt.onion/10.1007/s12539-020-00403-6 33387306!7776293!33387306     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Interdiscip+Sci 2021 ; 13 (1): 103-117 Nephropedia Template TP {{tp|p=33387306|t=2021. A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images |pdf=|usr=}}{{33387306}} gab.com Text A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images JO: Interdiscip Sci http://www.kidney.de/pget.php?&tw=1&pmid=33387306 #FOAvip Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2-97.6% overall accuracy without PCA and 97.6-100% with PCA for positive cases identification, respectively. Twit Text FOAVip A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images ????Interdiscip Sci http://www.kidney.de/pget.php?&tw=1&pmid=33387306 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33387306 #FOAvip English Wikipedia <ref>{{Cite pmid|33387306}}</ref> A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images #MMPMID33387306 Rasheed J; Hameed AA; Djeddi C; Jamil A; Al-Turjman F Interdiscip Sci 2021[Mar]; 13 (1): 103-117 PMID33387306show ga Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2-97.6% overall accuracy without PCA and 97.6-100% with PCA for positive cases identification, respectively. |*Imaging, Three-Dimensional[MESH] |*Machine Learning[MESH] |Algorithms[MESH] |COVID-19/*diagnosis/*diagnostic imaging/virology[MESH] |Databases as Topic[MESH] |Humans[MESH] |Logistic Models[MESH] |Neural Networks, Computer[MESH] |SARS-CoV-2/physiology[MESH] |Thorax/*diagnostic imaging[MESH]A machine learning-based framework for diagnosis of COVID-19 from ches(epmc).pdf DeepDyve Pubget Overpricing