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10.1016/j.compbiomed.2021.104252 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2021.104252 33610001!7966819!33610001     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 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 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Biol+Med 2021 ; 131 (ä): 104252 Nephropedia Template TP {{tp|p=33610001|t=2021. Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph |pdf=|usr=}}{{33610001}} gab.com Text Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33610001 #FOAvip BACKGROUND: Chest X-ray radiography (CXR) has been widely considered as an accessible, feasible, and convenient method to evaluate suspected patients' lung involvement during the COVID-19 pandemic. However, with the escalating number of suspected cases, traditional diagnosis via CXR fails to deliver results within a short period of time. Therefore, it is crucial to employ artificial intelligence (AI) to enhance CXRs for obtaining quick and accurate diagnoses. Previous studies have reported the feasibility of utilizing deep learning methods to screen for COVID-19 using CXR and CT results. However, these models only use a single deep learning network for chest radiograph detection; the accuracy of this approach required further improvement. METHODS: In this study, we propose a three-step hybrid ensemble model, including a feature extractor, a feature selector, and a classifier. First, a pre-trained AlexNet with an improved structure extracts the original image features. Then, the ReliefF algorithm is adopted to sort the extracted features, and a trial-and-error approach is used to select the n most important features to reduce the feature dimension. Finally, an SVM classifier provides classification results based on the n selected features. RESULTS: Compared to five existing models (InceptionV3: 97.916 +/- 0.408%; SqueezeNet: 97.189 +/- 0.526%; VGG19: 96.520 +/- 1.220%; ResNet50: 97.476 +/- 0.513%; ResNet101: 98.241 +/- 0.209%), the proposed model demonstrated the best performance in terms of overall accuracy rate (98.642 +/- 0.398%). Additionally, compared to the existing models, the proposed model demonstrates a considerable improvement in classification time efficiency (SqueezeNet: 6.602 +/- 0.001s; InceptionV3: 12.376 +/- 0.002s; ResNet50: 10.952 +/- 0.001s; ResNet101: 18.040 +/- 0.002s; VGG19: 16.632 +/- 0.002s; proposed model: 5.917 +/- 0.001s). CONCLUSION: The model proposed in this article is practical and effective, and can provide high-precision COVID-19 CXR detection. We demonstrated its suitability to aid medical professionals in distinguishing normal CXRs, viral pneumonia CXRs and COVID-19 CXRs efficiently on small sample sizes. Twit Text FOAVip Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33610001 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33610001 #FOAvip English Wikipedia <ref>{{Cite pmid|33610001}}</ref> Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph #MMPMID33610001 Jin W; Dong S; Dong C; Ye X Comput Biol Med 2021[Apr]; 131 (ä): 104252 PMID33610001show ga BACKGROUND: Chest X-ray radiography (CXR) has been widely considered as an accessible, feasible, and convenient method to evaluate suspected patients' lung involvement during the COVID-19 pandemic. However, with the escalating number of suspected cases, traditional diagnosis via CXR fails to deliver results within a short period of time. Therefore, it is crucial to employ artificial intelligence (AI) to enhance CXRs for obtaining quick and accurate diagnoses. Previous studies have reported the feasibility of utilizing deep learning methods to screen for COVID-19 using CXR and CT results. However, these models only use a single deep learning network for chest radiograph detection; the accuracy of this approach required further improvement. METHODS: In this study, we propose a three-step hybrid ensemble model, including a feature extractor, a feature selector, and a classifier. First, a pre-trained AlexNet with an improved structure extracts the original image features. Then, the ReliefF algorithm is adopted to sort the extracted features, and a trial-and-error approach is used to select the n most important features to reduce the feature dimension. Finally, an SVM classifier provides classification results based on the n selected features. RESULTS: Compared to five existing models (InceptionV3: 97.916 +/- 0.408%; SqueezeNet: 97.189 +/- 0.526%; VGG19: 96.520 +/- 1.220%; ResNet50: 97.476 +/- 0.513%; ResNet101: 98.241 +/- 0.209%), the proposed model demonstrated the best performance in terms of overall accuracy rate (98.642 +/- 0.398%). Additionally, compared to the existing models, the proposed model demonstrates a considerable improvement in classification time efficiency (SqueezeNet: 6.602 +/- 0.001s; InceptionV3: 12.376 +/- 0.002s; ResNet50: 10.952 +/- 0.001s; ResNet101: 18.040 +/- 0.002s; VGG19: 16.632 +/- 0.002s; proposed model: 5.917 +/- 0.001s). CONCLUSION: The model proposed in this article is practical and effective, and can provide high-precision COVID-19 CXR detection. We demonstrated its suitability to aid medical professionals in distinguishing normal CXRs, viral pneumonia CXRs and COVID-19 CXRs efficiently on small sample sizes. |*COVID-19/diagnostic imaging/epidemiology[MESH] |*Diagnosis, Computer-Assisted[MESH] |*Neural Networks, Computer[MESH] |*Pandemics[MESH] |*SARS-CoV-2[MESH] |*Tomography, X-Ray Computed[MESH] |Diagnosis, Differential[MESH] |Humans[MESH]Hybrid ensemble model for differential diagnosis between COVID-19 and (epmc).pdf DeepDyve Pubget Overpricing