Review



xception cnn architecture-based cnn model  (Aslan Pharmaceuticals)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    Aslan Pharmaceuticals xception cnn architecture-based cnn model
    Comparison of the recommended VGGCOV19-NET COVID-19 diagnosis method with other <t> CNN </t> methods developed using radiology images
    Xception Cnn Architecture Based Cnn Model, supplied by Aslan Pharmaceuticals, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/xception+cnn+architecture-based+cnn+model/xception+cnn+architecture+based+cnn+model/pmc08785935-472-11-58
    Average 90 stars, based on 1 article reviews
    xception cnn architecture-based cnn model - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm"

    Article Title: VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm

    Journal: Neural Computing & Applications

    doi: 10.1007/s00521-022-06918-x

    Comparison of the recommended VGGCOV19-NET COVID-19 diagnosis method with other CNN methods developed using radiology images
    Figure Legend Snippet: Comparison of the recommended VGGCOV19-NET COVID-19 diagnosis method with other CNN methods developed using radiology images

    Techniques Used: Comparison, Biomarker Discovery, Modification, Imaging

    Related Articles

    Comparison:

    Article Title: VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm
    Article Snippet: Khan et al. [ ] classified three classes with a new Xception CNN architecture-based CNN model with an accuracy ratio of 95%, Ahammed et al. [ ] classified three classes with the deep CNN model they created with accuracy of 94%, Apostolopoulos and Mpesiana [ ] classified three classes with the pre-trained VGG19 architecture with accuracy of 93.48%, Aslan et al. [ ] classified three classes with the hybrid CNN architecture with accuracy of 98.70%, Hira et al. [ ] classified three classes with the specially ResNeXt-50 with accuracy of 97.55%, and Wang et al. [ ] classified three classes with the deep CNN model they called COVID-Net with an accuracy of 93.3%.

    Biomarker Discovery:

    Article Title: VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm
    Article Snippet: Khan et al. [ ] classified three classes with a new Xception CNN architecture-based CNN model with an accuracy ratio of 95%, Ahammed et al. [ ] classified three classes with the deep CNN model they created with accuracy of 94%, Apostolopoulos and Mpesiana [ ] classified three classes with the pre-trained VGG19 architecture with accuracy of 93.48%, Aslan et al. [ ] classified three classes with the hybrid CNN architecture with accuracy of 98.70%, Hira et al. [ ] classified three classes with the specially ResNeXt-50 with accuracy of 97.55%, and Wang et al. [ ] classified three classes with the deep CNN model they called COVID-Net with an accuracy of 93.3%.

    Modification:

    Article Title: VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm
    Article Snippet: Khan et al. [ ] classified three classes with a new Xception CNN architecture-based CNN model with an accuracy ratio of 95%, Ahammed et al. [ ] classified three classes with the deep CNN model they created with accuracy of 94%, Apostolopoulos and Mpesiana [ ] classified three classes with the pre-trained VGG19 architecture with accuracy of 93.48%, Aslan et al. [ ] classified three classes with the hybrid CNN architecture with accuracy of 98.70%, Hira et al. [ ] classified three classes with the specially ResNeXt-50 with accuracy of 97.55%, and Wang et al. [ ] classified three classes with the deep CNN model they called COVID-Net with an accuracy of 93.3%.

    Imaging:

    Article Title: VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm
    Article Snippet: Khan et al. [ ] classified three classes with a new Xception CNN architecture-based CNN model with an accuracy ratio of 95%, Ahammed et al. [ ] classified three classes with the deep CNN model they created with accuracy of 94%, Apostolopoulos and Mpesiana [ ] classified three classes with the pre-trained VGG19 architecture with accuracy of 93.48%, Aslan et al. [ ] classified three classes with the hybrid CNN architecture with accuracy of 98.70%, Hira et al. [ ] classified three classes with the specially ResNeXt-50 with accuracy of 97.55%, and Wang et al. [ ] classified three classes with the deep CNN model they called COVID-Net with an accuracy of 93.3%.



    Similar Products

    xception cnn architecture-based cnn model  (Aslan Pharmaceuticals)
    90
    Aslan Pharmaceuticals xception cnn architecture-based cnn model
    Comparison of the recommended VGGCOV19-NET COVID-19 diagnosis method with other <t> CNN </t> methods developed using radiology images
    Xception Cnn Architecture Based Cnn Model, supplied by Aslan Pharmaceuticals, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/xception+cnn+architecture-based+cnn+model/xception+cnn+architecture+based+cnn+model/pmc08785935-472-11-58
    Average 90 stars, based on 1 article reviews
    xception cnn architecture-based cnn model - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Comparison of the recommended VGGCOV19-NET COVID-19 diagnosis method with other CNN methods developed using radiology images

    Journal: Neural Computing & Applications

    Article Title: VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm

    doi: 10.1007/s00521-022-06918-x

    Figure Lengend Snippet: Comparison of the recommended VGGCOV19-NET COVID-19 diagnosis method with other CNN methods developed using radiology images

    Article Snippet: Khan et al. [ ] classified three classes with a new Xception CNN architecture-based CNN model with an accuracy ratio of 95%, Ahammed et al. [ ] classified three classes with the deep CNN model they created with accuracy of 94%, Apostolopoulos and Mpesiana [ ] classified three classes with the pre-trained VGG19 architecture with accuracy of 93.48%, Aslan et al. [ ] classified three classes with the hybrid CNN architecture with accuracy of 98.70%, Hira et al. [ ] classified three classes with the specially ResNeXt-50 with accuracy of 97.55%, and Wang et al. [ ] classified three classes with the deep CNN model they called COVID-Net with an accuracy of 93.3%.

    Techniques: Comparison, Biomarker Discovery, Modification, Imaging