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10.4258/hir.2021.27.1.82 http://scihub22266oqcxt.onion/10.4258/hir.2021.27.1.82 33611880!7921566!33611880     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       Healthc+Inform+Res 2021 ; 27 (1): 82-91 Nephropedia Template TP {{tp|p=33611880|t=2021. Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform |pdf=|usr=}}{{33611880}} gab.com Text Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform JO: Healthc Inform Res http://www.kidney.de/pget.php?&tw=1&pmid=33611880 #FOAvip OBJECTIVES: This paper proposes a method for computer-assisted diagnosis of coronavirus disease 2019 (COVID-19) through chest X-ray imaging using a deep learning model without writing a single line of code using the Konstanz Information Miner (KNIME) analytics platform. METHODS: We obtained 155 samples of posteroanterior chest X-ray images from COVID-19 open dataset repositories to develop a classification model using a simple convolutional neural network (CNN). All of the images contained diagnostic information for COVID-19 and other diseases. The model would classify whether a patient was infected with COVID-19 or not. Eighty percent of the images were used for model training, and the rest were used for testing. The graphic user interface-based programming in the KNIME enabled class label annotation, data preprocessing, CNN model training and testing, performance evaluation, and so on. RESULTS: 1,000 epochs training were performed to test the simple CNN model. The lower and upper bounds of positive predictive value (precision), sensitivity (recall), specificity, and f-measure are 92.3% and 94.4%. Both bounds of the model's accuracies were equal to 93.5% and 96.6% of the area under the receiver operating characteristic curve for the test set. CONCLUSIONS: In this study, a researcher who does not have basic knowledge of python programming successfully performed deep learning analysis of chest x-ray image dataset using the KNIME independently. The KNIME will reduce the time spent and lower the threshold for deep learning research applied to healthcare. Twit Text FOAVip Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform ????Healthc Inform Res http://www.kidney.de/pget.php?&tw=1&pmid=33611880 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33611880 #FOAvip English Wikipedia <ref>{{Cite pmid|33611880}}</ref> Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform #MMPMID33611880 An JY; Seo H; Kim YG; Lee KE; Kim S; Kong HJ Healthc Inform Res 2021[Jan]; 27 (1): 82-91 PMID33611880show ga OBJECTIVES: This paper proposes a method for computer-assisted diagnosis of coronavirus disease 2019 (COVID-19) through chest X-ray imaging using a deep learning model without writing a single line of code using the Konstanz Information Miner (KNIME) analytics platform. METHODS: We obtained 155 samples of posteroanterior chest X-ray images from COVID-19 open dataset repositories to develop a classification model using a simple convolutional neural network (CNN). All of the images contained diagnostic information for COVID-19 and other diseases. The model would classify whether a patient was infected with COVID-19 or not. Eighty percent of the images were used for model training, and the rest were used for testing. The graphic user interface-based programming in the KNIME enabled class label annotation, data preprocessing, CNN model training and testing, performance evaluation, and so on. RESULTS: 1,000 epochs training were performed to test the simple CNN model. The lower and upper bounds of positive predictive value (precision), sensitivity (recall), specificity, and f-measure are 92.3% and 94.4%. Both bounds of the model's accuracies were equal to 93.5% and 96.6% of the area under the receiver operating characteristic curve for the test set. CONCLUSIONS: In this study, a researcher who does not have basic knowledge of python programming successfully performed deep learning analysis of chest x-ray image dataset using the KNIME independently. The KNIME will reduce the time spent and lower the threshold for deep learning research applied to healthcare. äCodeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIM(epmc).pdf DeepDyve Pubget Overpricing