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xception model trained on  (Kaggle Inc)

 
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    Kaggle Inc xception model trained on
    Xception Model Trained On, supplied by Kaggle Inc, 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/result/xception model trained on/product/Kaggle Inc
    Average 90 stars, based on 1 article reviews
    xception model trained on - by Bioz Stars, 2026-03
    90/100 stars

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    Image Search Results


    A summary of DR prescreening techniques and reported performances.

    Journal: Scientific Reports

    Article Title: Diabetic retinopathy detection via exudates and hemorrhages segmentation using iterative NICK thresholding, watershed, and Chi 2 feature ranking

    doi: 10.1038/s41598-025-90048-6

    Figure Lengend Snippet: A summary of DR prescreening techniques and reported performances.

    Article Snippet: Bhuiyan et al. , The cloud-based screening model was developed. Neural network architectures and logistic model trees: Xception, Inception-V3, Inception-Resnet-V2 and Logistic model trees (LMT) were employed , 88,702 images from Kaggle dataset , Sensitivity , 99.21.

    Techniques: Biomarker Discovery, Plasmid Preparation, Diagnostic Assay, Software, Extraction, Selection

    Comparison of state-of-the-art studies on breast cancer detection and federated learning.

    Journal: Healthcare

    Article Title: A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

    doi: 10.3390/healthcare11243185

    Figure Lengend Snippet: Comparison of state-of-the-art studies on breast cancer detection and federated learning.

    Article Snippet: [ ] , DL-based Xception model , Kaggle dataset , Model exhibited proficiency in breast cancer detection , Increased processing time not discussed.

    Techniques: Comparison, Diagnostic Assay, Histopathology

    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