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10.1016/j.bbe.2021.06.011 http://scihub22266oqcxt.onion/10.1016/j.bbe.2021.06.011 34257471!8264565!34257471     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 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       Biocybern+Biomed+Eng 2021 ; 41 (3): 1025-1038 Nephropedia Template TP {{tp|p=34257471|t=2021. FractalCovNet architecture for COVID-19 Chest X-ray image Classification and CT-scan image Segmentation |pdf=|usr=}}{{34257471}} gab.com Text FractalCovNet architecture for COVID-19 Chest X-ray image Classification and CT-scan image Segmentation JO: Biocybern Biomed Eng http://www.kidney.de/pget.php?&tw=1&pmid=34257471 #FOAvip Precise and fast diagnosis of COVID-19 cases play a vital role in early stage of medical treatment and prevention. Automatic detection of COVID-19 cases using the chest X-ray images and chest CT-scan images will be helpful to reduce the impact of this pandemic on the human society. We have developed a novel FractalCovNet architecture using Fractal blocks and U-Net for segmentation of chest CT-scan images to localize the lesion region. The same FractalCovNet architecture is also used for classification of chest X-ray images using transfer learning. We have compared the segmentation results using various model such as U-Net, DenseUNet, Segnet, ResnetUNet, and FCN. We have also compared the classification results with various models like ResNet5-, Xception, InceptionResNetV2, VGG-16 and DenseNet architectures. The proposed FractalCovNet model is able to predict the COVID-19 lesion with high F-measure and precision values compared to the other state-of-the-art methods. Thus the proposed model can accurately predict the COVID-19 cases and discover lesion regions in chest CT without the manual annotations of lesions for every suspected individual. An easily-trained and high-performance deep learning model provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-II-COV. Twit Text FOAVip FractalCovNet architecture for COVID-19 Chest X-ray image Classification and CT-scan image Segmentation ????Biocybern Biomed Eng http://www.kidney.de/pget.php?&tw=1&pmid=34257471 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34257471 #FOAvip English Wikipedia <ref>{{Cite pmid|34257471}}</ref> FractalCovNet architecture for COVID-19 Chest X-ray image Classification and CT-scan image Segmentation #MMPMID34257471 Munusamy H; Karthikeyan JM; Shriram G; Thanga Revathi S; Aravindkumar S Biocybern Biomed Eng 2021[Jul]; 41 (3): 1025-1038 PMID34257471show ga Precise and fast diagnosis of COVID-19 cases play a vital role in early stage of medical treatment and prevention. Automatic detection of COVID-19 cases using the chest X-ray images and chest CT-scan images will be helpful to reduce the impact of this pandemic on the human society. We have developed a novel FractalCovNet architecture using Fractal blocks and U-Net for segmentation of chest CT-scan images to localize the lesion region. The same FractalCovNet architecture is also used for classification of chest X-ray images using transfer learning. We have compared the segmentation results using various model such as U-Net, DenseUNet, Segnet, ResnetUNet, and FCN. We have also compared the classification results with various models like ResNet5-, Xception, InceptionResNetV2, VGG-16 and DenseNet architectures. The proposed FractalCovNet model is able to predict the COVID-19 lesion with high F-measure and precision values compared to the other state-of-the-art methods. Thus the proposed model can accurately predict the COVID-19 cases and discover lesion regions in chest CT without the manual annotations of lesions for every suspected individual. An easily-trained and high-performance deep learning model provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-II-COV. äFractalCovNet architecture for COVID-19 Chest X-ray image Classificati(epmc).pdf DeepDyve Pubget Overpricing