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10.1016/j.asoc.2020.106580 http://scihub22266oqcxt.onion/10.1016/j.asoc.2020.106580 32837453!7385069!32837453     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 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 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Appl+Soft+Comput 2020 ; 97 (ä): 106580 Nephropedia Template TP {{tp|p=32837453|t=2020. A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization |pdf=|usr=}}{{32837453}} gab.com Text A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization JO: Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=32837453 #FOAvip A pneumonia of unknown causes, which was detected in Wuhan, China, and spread rapidly throughout the world, was declared as Coronavirus disease 2019 (COVID-19). Thousands of people have lost their lives to this disease. Its negative effects on public health are ongoing. In this study, an intelligence computer-aided model that can automatically detect positive COVID-19 cases is proposed to support daily clinical applications. The proposed model is based on the convolution neural network (CNN) architecture and can automatically reveal discriminative features on chest X-ray images through its convolution with rich filter families, abstraction, and weight-sharing characteristics. Contrary to the generally used transfer learning approach, the proposed deep CNN model was trained from scratch. Instead of the pre-trained CNNs, a novel serial network consisting of five convolution layers was designed. This CNN model was utilized as a deep feature extractor. The extracted deep discriminative features were used to feed the machine learning algorithms, which were k-nearest neighbor, support vector machine (SVM), and decision tree. The hyperparameters of the machine learning models were optimized using the Bayesian optimization algorithm. The experiments were conducted on a public COVID-19 radiology database. The database was divided into two parts as training and test sets with 70% and 30% rates, respectively. As a result, the most efficient results were ensured by the SVM classifier with an accuracy of 98.97%, a sensitivity of 89.39%, a specificity of 99.75%, and an F-score of 96.72%. Consequently, a cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection. The developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process. Thanks to the proposed tool, the misdiagnosis rates can be reduced, and the proposed model can be used as a retrospective evaluation tool to validate positive COVID-19 infection cases. Twit Text FOAVip A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization ????Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=32837453 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32837453 #FOAvip English Wikipedia <ref>{{Cite pmid|32837453}}</ref> A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization #MMPMID32837453 Nour M; Comert Z; Polat K Appl Soft Comput 2020[Dec]; 97 (ä): 106580 PMID32837453show ga A pneumonia of unknown causes, which was detected in Wuhan, China, and spread rapidly throughout the world, was declared as Coronavirus disease 2019 (COVID-19). Thousands of people have lost their lives to this disease. Its negative effects on public health are ongoing. In this study, an intelligence computer-aided model that can automatically detect positive COVID-19 cases is proposed to support daily clinical applications. The proposed model is based on the convolution neural network (CNN) architecture and can automatically reveal discriminative features on chest X-ray images through its convolution with rich filter families, abstraction, and weight-sharing characteristics. Contrary to the generally used transfer learning approach, the proposed deep CNN model was trained from scratch. Instead of the pre-trained CNNs, a novel serial network consisting of five convolution layers was designed. This CNN model was utilized as a deep feature extractor. The extracted deep discriminative features were used to feed the machine learning algorithms, which were k-nearest neighbor, support vector machine (SVM), and decision tree. The hyperparameters of the machine learning models were optimized using the Bayesian optimization algorithm. The experiments were conducted on a public COVID-19 radiology database. The database was divided into two parts as training and test sets with 70% and 30% rates, respectively. As a result, the most efficient results were ensured by the SVM classifier with an accuracy of 98.97%, a sensitivity of 89.39%, a specificity of 99.75%, and an F-score of 96.72%. Consequently, a cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection. The developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process. Thanks to the proposed tool, the misdiagnosis rates can be reduced, and the proposed model can be used as a retrospective evaluation tool to validate positive COVID-19 infection cases. äA Novel Medical Diagnosis model for COVID-19 infection detection based(epmc).pdf DeepDyve Pubget Overpricing