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10.1109/JBHI.2021.3069798 http://scihub22266oqcxt.onion/10.1109/JBHI.2021.3069798 33788696!8768975!33788696     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 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2021 ; 25 (6): 1852-1863 Nephropedia Template TP {{tp|p=33788696|t=2021. Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns |pdf=|usr=}}{{33788696}} gab.com Text Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=33788696 #FOAvip The coronavirus (COVID-19) pandemic has been adversely affecting people's health globally. To diminish the effect of this widespread pandemic, it is essential to detect COVID-19 cases as quickly as possible. Chest radiographs are less expensive and are a widely available imaging modality for detecting chest pathology compared with CT images. They play a vital role in early prediction and developing treatment plans for suspected or confirmed COVID-19 chest infection patients. In this paper, a novel shape-dependent Fibonacci-p patterns-based feature descriptor using a machine learning approach is proposed. Computer simulations show that the presented system (1) increases the effectiveness of differentiating COVID-19, viral pneumonia, and normal conditions, (2) is effective on small datasets, and (3) has faster inference time compared to deep learning methods with comparable performance. Computer simulations are performed on two publicly available datasets; (a) the Kaggle dataset, and (b) the COVIDGR dataset. To assess the performance of the presented system, various evaluation parameters, such as accuracy, recall, specificity, precision, and f1-score are used. Nearly 100% differentiation between normal and COVID-19 radiographs is observed for the three-class classification scheme using the lung area-specific Kaggle radiographs. While Recall of 72.65 +/- 6.83 and specificity of 77.72 +/- 8.06 is observed for the COVIDGR dataset. Twit Text FOAVip Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=33788696 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33788696 #FOAvip English Wikipedia <ref>{{Cite pmid|33788696}}</ref> Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns #MMPMID33788696 Panetta K; Sanghavi F; Agaian S; Madan N IEEE J Biomed Health Inform 2021[Jun]; 25 (6): 1852-1863 PMID33788696show ga The coronavirus (COVID-19) pandemic has been adversely affecting people's health globally. To diminish the effect of this widespread pandemic, it is essential to detect COVID-19 cases as quickly as possible. Chest radiographs are less expensive and are a widely available imaging modality for detecting chest pathology compared with CT images. They play a vital role in early prediction and developing treatment plans for suspected or confirmed COVID-19 chest infection patients. In this paper, a novel shape-dependent Fibonacci-p patterns-based feature descriptor using a machine learning approach is proposed. Computer simulations show that the presented system (1) increases the effectiveness of differentiating COVID-19, viral pneumonia, and normal conditions, (2) is effective on small datasets, and (3) has faster inference time compared to deep learning methods with comparable performance. Computer simulations are performed on two publicly available datasets; (a) the Kaggle dataset, and (b) the COVIDGR dataset. To assess the performance of the presented system, various evaluation parameters, such as accuracy, recall, specificity, precision, and f1-score are used. Nearly 100% differentiation between normal and COVID-19 radiographs is observed for the three-class classification scheme using the lung area-specific Kaggle radiographs. While Recall of 72.65 +/- 6.83 and specificity of 77.72 +/- 8.06 is observed for the COVIDGR dataset. |*Pattern Recognition, Automated[MESH] |Automation[MESH] |COVID-19/*diagnostic imaging/virology[MESH] |Computer Simulation[MESH] |Humans[MESH] |Machine Learning[MESH] |Pneumonia, Viral/*diagnostic imaging/virology[MESH] |Radiography, Thoracic[MESH] |SARS-CoV-2/isolation & purification[MESH] |Sensitivity and Specificity[MESH]Automated Detection of COVID-19 Cases on Radiographs using Shape-Depen(epmc).pdf DeepDyve Pubget Overpricing