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10.1007/s42979-021-00531-w http://scihub22266oqcxt.onion/10.1007/s42979-021-00531-w 33754141!7968411!33754141     free     free     free       SN+Comput+Sci 2021 ; 2 (3): 145 Nephropedia Template TP {{tp|p=33754141|t=2021. CvDeep-COVID-19 Detection Model |pdf=|usr=}}{{33754141}} gab.com Text CvDeep-COVID-19 Detection Model JO: SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33754141 #FOAvip COVID-19 (Coronavirus disease) has made world stand still. Detection of COVID-19 positive case immediately is requirement for prevention of its spread and save lives. X-ray images comprises substantial data about the spread of infection through virus in lungs. Advanced assistive tools using machine learning overcome the problem of lack of medical facilities in remote places. In this research, CvDeep, a model for COVID-19 detection using X-ray images as resource is designed. The images are preprocessed for final diagnosis with pertained models. It is observed that it is difficult to detect COVID-19 in early stage using images analysis, but if pre trained deep learning models are used, it can improve the accuracy of detection. This model provides accuracy of 95% for COVID-19 cases. The models used for prediction are AlexNet, SquzeeNet, ResNet and DenseNet. The data set can be shared online to assist radiologists. Patients with COVID-19 (+ ve) can be given instant hospitalization without waiting for lab test result so that survival rate can be increased. Model is evaluated by expert radiologists. Twit Text FOAVip CvDeep-COVID-19 Detection Model ????SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33754141 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33754141 #FOAvip English Wikipedia <ref>{{Cite pmid|33754141}}</ref> CvDeep-COVID-19 Detection Model #MMPMID33754141 Ingle VA; Ambad PM SN Comput Sci 2021[]; 2 (3): 145 PMID33754141show ga COVID-19 (Coronavirus disease) has made world stand still. Detection of COVID-19 positive case immediately is requirement for prevention of its spread and save lives. X-ray images comprises substantial data about the spread of infection through virus in lungs. Advanced assistive tools using machine learning overcome the problem of lack of medical facilities in remote places. In this research, CvDeep, a model for COVID-19 detection using X-ray images as resource is designed. The images are preprocessed for final diagnosis with pertained models. It is observed that it is difficult to detect COVID-19 in early stage using images analysis, but if pre trained deep learning models are used, it can improve the accuracy of detection. This model provides accuracy of 95% for COVID-19 cases. The models used for prediction are AlexNet, SquzeeNet, ResNet and DenseNet. The data set can be shared online to assist radiologists. Patients with COVID-19 (+ ve) can be given instant hospitalization without waiting for lab test result so that survival rate can be increased. Model is evaluated by expert radiologists. äCvDeep-COVID-19 Detection Model (epmc).pdf DeepDyve Pubget Overpricing