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10.1155/2021/5522729 http://scihub22266oqcxt.onion/10.1155/2021/5522729 33968281!8063851!33968281     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       Dis+Markers 2021 ; 2021 (ä): 5522729 Nephropedia Template TP {{tp|p=33968281|t=2021. Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques |pdf=|usr=}}{{33968281}} gab.com Text Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques JO: Dis Markers http://www.kidney.de/pget.php?&tw=1&pmid=33968281 #FOAvip Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of infection. Radiological analysis of CT images has given higher prediction rate when compared to RT-PCR technique. In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-Acquired Pneumonia (CAP) CT images, and normal CT images with high specificity and sensitivity. The proposed system in this paper has been compared with various machine learning classifiers and other deep learning classifiers for better data analysis. The outcome of this study is also compared with other studies which were carried out recently on COVID-19 classification for further analysis. The proposed model has been found to outperform with an accuracy of 96.69%, sensitivity of 96%, and specificity of 98%. Twit Text FOAVip Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques ????Dis Markers http://www.kidney.de/pget.php?&tw=1&pmid=33968281 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33968281 #FOAvip English Wikipedia <ref>{{Cite pmid|33968281}}</ref> Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques #MMPMID33968281 Perumal V; Narayanan V; Rajasekar SJS Dis Markers 2021[]; 2021 (ä): 5522729 PMID33968281show ga Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of infection. Radiological analysis of CT images has given higher prediction rate when compared to RT-PCR technique. In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-Acquired Pneumonia (CAP) CT images, and normal CT images with high specificity and sensitivity. The proposed system in this paper has been compared with various machine learning classifiers and other deep learning classifiers for better data analysis. The outcome of this study is also compared with other studies which were carried out recently on COVID-19 classification for further analysis. The proposed model has been found to outperform with an accuracy of 96.69%, sensitivity of 96%, and specificity of 98%. |*Machine Learning[MESH] |Bayes Theorem[MESH] |COVID-19/*diagnostic imaging[MESH] |Case-Control Studies[MESH] |Community-Acquired Infections/diagnostic imaging[MESH] |Decision Trees[MESH] |Humans[MESH] |Lung/*diagnostic imaging[MESH] |Models, Statistical[MESH] |Pneumonia/diagnostic imaging[MESH] |Sensitivity and Specificity[MESH]Prediction of COVID-19 with Computed Tomography Images using Hybrid Le(epmc).pdf DeepDyve Pubget Overpricing