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10.1007/s12652-021-02917-3 http://scihub22266oqcxt.onion/10.1007/s12652-021-02917-3 33527000!7841043!33527000     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33527000&cmd=llinks): Failed to open stream: HTTP request failed! 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COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images |pdf=|usr=}}{{33527000}} gab.com Text COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images JO: J Ambient Intell Humaniz Comput http://www.kidney.de/pget.php?&tw=1&pmid=33527000 #FOAvip COVID-19 pandemic is widely spreading over the entire world and has established significant community spread. Fostering a prediction system can help prepare the officials to respond properly and quickly. Medical imaging like X-ray and computed tomography (CT) can play an important role in the early prediction of COVID-19 patients that will help the timely treatment of the patients. The x-ray images from COVID-19 patients reveal the pneumonia infections that can be used to identify the patients of COVID-19. This study presents the use of Convolutional Neural Network (CNN) that extracts the features from chest x-ray images for the prediction. Three filters are applied to get the edges from the images that help to get the desired segmented target with the infected area of the x-ray. To cope with the smaller size of the training dataset, Keras' ImageDataGenerator class is used to generate ten thousand augmented images. Classification is performed with two, three, and four classes where the four-class problem has X-ray images from COVID-19, normal people, virus pneumonia, and bacterial pneumonia. Results demonstrate that the proposed CNN model can predict COVID-19 patients with high accuracy. It can help automate screening of the patients for COVID-19 with minimal contact, especially areas where the influx of patients can not be treated by the available medical staff. The performance comparison of the proposed approach with VGG16 and AlexNet shows that classification results for two and four classes are competitive and identical for three-class classification. Twit Text FOAVip COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images ????J Ambient Intell Humaniz Comput http://www.kidney.de/pget.php?&tw=1&pmid=33527000 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33527000 #FOAvip English Wikipedia <ref>{{Cite pmid|33527000}}</ref> COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images #MMPMID33527000 Umer M; Ashraf I; Ullah S; Mehmood A; Choi GS J Ambient Intell Humaniz Comput 2022[]; 13 (1): 535-547 PMID33527000show ga COVID-19 pandemic is widely spreading over the entire world and has established significant community spread. Fostering a prediction system can help prepare the officials to respond properly and quickly. Medical imaging like X-ray and computed tomography (CT) can play an important role in the early prediction of COVID-19 patients that will help the timely treatment of the patients. The x-ray images from COVID-19 patients reveal the pneumonia infections that can be used to identify the patients of COVID-19. This study presents the use of Convolutional Neural Network (CNN) that extracts the features from chest x-ray images for the prediction. Three filters are applied to get the edges from the images that help to get the desired segmented target with the infected area of the x-ray. To cope with the smaller size of the training dataset, Keras' ImageDataGenerator class is used to generate ten thousand augmented images. Classification is performed with two, three, and four classes where the four-class problem has X-ray images from COVID-19, normal people, virus pneumonia, and bacterial pneumonia. Results demonstrate that the proposed CNN model can predict COVID-19 patients with high accuracy. It can help automate screening of the patients for COVID-19 with minimal contact, especially areas where the influx of patients can not be treated by the available medical staff. The performance comparison of the proposed approach with VGG16 and AlexNet shows that classification results for two and four classes are competitive and identical for three-class classification. äCOVINet: a convolutional neural network approach for predicting COVID-(epmc).pdf DeepDyve Pubget Overpricing