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10.1117/1.JMI.8.S1.014001 http://scihub22266oqcxt.onion/10.1117/1.JMI.8.S1.014001 33457446!7804292!33457446     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Med+Imaging+(Bellingham) 2021 ; 8 (Suppl 1): 014001 Nephropedia Template TP {{tp|p=33457446|t=2021. COVID-19 detection and heatmap generation in chest x-ray images |pdf=|usr=}}{{33457446}} gab.com Text COVID-19 detection and heatmap generation in chest x-ray images JO: J Med Imaging (Bellingham) http://www.kidney.de/pget.php?&tw=1&pmid=33457446 #FOAvip Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of 1500 x 1500 x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages. Twit Text FOAVip COVID-19 detection and heatmap generation in chest x-ray images ????J Med Imaging (Bellingham) http://www.kidney.de/pget.php?&tw=1&pmid=33457446 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33457446 #FOAvip English Wikipedia <ref>{{Cite pmid|33457446}}</ref> COVID-19 detection and heatmap generation in chest x-ray images #MMPMID33457446 Kusakunniran W; Karnjanapreechakorn S; Siriapisith T; Borwarnginn P; Sutassananon K; Tongdee T; Saiviroonporn P J Med Imaging (Bellingham) 2021[Jan]; 8 (Suppl 1): 014001 PMID33457446show ga Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of 1500 x 1500 x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages. äCOVID-19 detection and heatmap generation in chest x-ray images (epmc).pdf DeepDyve Pubget Overpricing