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10.1155/2021/6649591 http://scihub22266oqcxt.onion/10.1155/2021/6649591 33747417!7954614!33747417     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       J+Healthc+Eng 2021 ; 2021 (ä): 6649591 Nephropedia Template TP {{tp|p=33747417|t=2021. Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion |pdf=|usr=}}{{33747417}} gab.com Text Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion JO: J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=33747417 #FOAvip Coronavirus disease (COVID-19) is highly contagious and pathogenic. Currently, the diagnosis of COVID-19 is based on nucleic acid testing, but it has false negatives and hysteresis. The use of lung CT scans can help screen and effectively monitor diagnosed cases. The application of computer-aided diagnosis technology can reduce the burden on doctors, which is conducive to rapid and large-scale diagnostic screening. In this paper, we proposed an automatic detection method for COVID-19 based on spatiotemporal information fusion. Using the segmentation network in the deep learning method to segment the lung area and the lesion area, the spatiotemporal information features of multiple CT scans are extracted to perform auxiliary diagnosis analysis. The performance of this method was verified on the collected dataset. We achieved the classification of COVID-19 CT scans and non-COVID-19 CT scans and analyzed the development of the patients' condition through the CT scans. The average accuracy rate is 96.7%, sensitivity is 95.2%, and F1 score is 95.9%. Each scan takes about 30 seconds for detection. Twit Text FOAVip Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion ????J Healthc Eng http://www.kidney.de/pget.php?&tw=1&pmid=33747417 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33747417 #FOAvip English Wikipedia <ref>{{Cite pmid|33747417}}</ref> Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion #MMPMID33747417 Li T; Wei W; Cheng L; Zhao S; Xu C; Zhang X; Zeng Y; Gu J J Healthc Eng 2021[]; 2021 (ä): 6649591 PMID33747417show ga Coronavirus disease (COVID-19) is highly contagious and pathogenic. Currently, the diagnosis of COVID-19 is based on nucleic acid testing, but it has false negatives and hysteresis. The use of lung CT scans can help screen and effectively monitor diagnosed cases. The application of computer-aided diagnosis technology can reduce the burden on doctors, which is conducive to rapid and large-scale diagnostic screening. In this paper, we proposed an automatic detection method for COVID-19 based on spatiotemporal information fusion. Using the segmentation network in the deep learning method to segment the lung area and the lesion area, the spatiotemporal information features of multiple CT scans are extracted to perform auxiliary diagnosis analysis. The performance of this method was verified on the collected dataset. We achieved the classification of COVID-19 CT scans and non-COVID-19 CT scans and analyzed the development of the patients' condition through the CT scans. The average accuracy rate is 96.7%, sensitivity is 95.2%, and F1 score is 95.9%. Each scan takes about 30 seconds for detection. |*Radiographic Image Interpretation, Computer-Assisted[MESH] |*Tomography, X-Ray Computed[MESH] |Algorithms[MESH] |COVID-19/*diagnostic imaging[MESH] |Deep Learning[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |SARS-CoV-2[MESH]Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal (epmc).pdf DeepDyve Pubget Overpricing