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xception  (Kaggle Inc)

 
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    Kaggle Inc xception
    Xception, 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/product/Kaggle Inc
    Average 90 stars, based on 1 article reviews
    xception - by Bioz Stars, 2026-04
    90/100 stars

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    Image Search Results


    Flow of the Xception architecture, illustrating the depthwise separable convolutions that optimize computational efficiency while maintaining high accuracy .

    Journal: Life

    Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

    doi: 10.3390/life15030327

    Figure Lengend Snippet: Flow of the Xception architecture, illustrating the depthwise separable convolutions that optimize computational efficiency while maintaining high accuracy .

    Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

    Techniques:

    Performance assessment of transfer learning: Xception.

    Journal: Life

    Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

    doi: 10.3390/life15030327

    Figure Lengend Snippet: Performance assessment of transfer learning: Xception.

    Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

    Techniques:

    Performance matrices for base model + transfer learning.

    Journal: Life

    Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

    doi: 10.3390/life15030327

    Figure Lengend Snippet: Performance matrices for base model + transfer learning.

    Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

    Techniques: Biomarker Discovery

    Performance assessment of fine-tuned transfer learning: Xception.

    Journal: Life

    Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

    doi: 10.3390/life15030327

    Figure Lengend Snippet: Performance assessment of fine-tuned transfer learning: Xception.

    Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

    Techniques:

    Performance matrices for fine-tuned transfer learning model.

    Journal: Life

    Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

    doi: 10.3390/life15030327

    Figure Lengend Snippet: Performance matrices for fine-tuned transfer learning model.

    Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

    Techniques: Biomarker Discovery

    Comparison with other state-of-art model.

    Journal: Life

    Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

    doi: 10.3390/life15030327

    Figure Lengend Snippet: Comparison with other state-of-art model.

    Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

    Techniques: Comparison

    Prediction for tumor (1/0) using fine-tuned transfer learning: Xception.

    Journal: Life

    Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

    doi: 10.3390/life15030327

    Figure Lengend Snippet: Prediction for tumor (1/0) using fine-tuned transfer learning: Xception.

    Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

    Techniques:

    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