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Showing 1 - 20 results of 1,667 for search '(( learning ((ai decrease) OR (_ decrease)) ) OR ( ct ((largest decrease) OR (larger decrease)) ))', query time: 0.49s Refine Results
  1. 1
    Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis.

    Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis. by M. Mendez (6624953)

    Published 2025
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  2. 2
    Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis.

    Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis. by M. Mendez (6624953)

    Published 2025
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  3. 3
    AI driven topic clustering and trend analysis workflow.

    AI driven topic clustering and trend analysis workflow. by Min Ho An (7894658)

    Published 2025
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  4. 4
    Decrease in loss during the training process.

    Decrease in loss during the training process. by Muhammad Idrees (92773)

    Published 2025
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  5. 5
    Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. This table offers a discussion of the measures of high interest in clinical implementation. Sensitivity (true positive rate) reflects the ability to correctly identify patients with CVD, while Specificity (true negative rate) indicates the ability to correctly rule out patients without CVD. The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power. The suggested model has the best balance of the greatest sensitivity and specificity, as well as the least Negative Likelihood Ratio, which highlights its strength and better performance as a diagnostic tool in the clinical practice.

    Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs.... by Medhat A. Tawfeek (22522087)

    Published 2025
    “…<p>Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. …”
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  6. 6
    Architecture of Multi-Layer U-Net Model.

    Architecture of Multi-Layer U-Net Model. by Muhammad Zubair (728141)

    Published 2025
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  7. 7
    GM Mask image count after preprocessing.

    GM Mask image count after preprocessing. by Muhammad Zubair (728141)

    Published 2025
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  8. 8
    Proposed Multi-Layer U-Net model for gray matter segmentation.

    Proposed Multi-Layer U-Net model for gray matter segmentation. by Muhammad Zubair (728141)

    Published 2025
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  9. 9
    Gray Matter segmentation results using the proposed Multi-layer U-Net Model.

    Gray Matter segmentation results using the proposed Multi-layer U-Net Model. by Muhammad Zubair (728141)

    Published 2025
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  10. 10
    Gray matter segmentation results using the Attention U-Net model.

    Gray matter segmentation results using the Attention U-Net model. by Muhammad Zubair (728141)

    Published 2025
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  11. 11
    Gray matter segmented views of the brain.

    Gray matter segmented views of the brain. by Muhammad Zubair (728141)

    Published 2025
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  12. 12
    Performance metrics of the standard U-Net (Vanilla U-Net) for the brain segmentation.

    Performance metrics of the standard U-Net (Vanilla U-Net) for the brain segmentation. by Muhammad Zubair (728141)

    Published 2025
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  13. 13
    Architecture of Multi-EfficientNet.

    Architecture of Multi-EfficientNet. by Muhammad Zubair (728141)

    Published 2025
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  14. 14
    Average Saliency Map Results.

    Average Saliency Map Results. by Muhammad Zubair (728141)

    Published 2025
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  15. 15
    Visual comparison of Brain segmentation results.

    Visual comparison of Brain segmentation results. by Muhammad Zubair (728141)

    Published 2025
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  16. 16
    Comparative evaluation of gray matter segmentation using Multi-Layer U-Net, Vanilla U-Net, Attention U-Net, and state-of-the-art methods. The results of the proposed model are calculated on the test set.

    Comparative evaluation of gray matter segmentation using Multi-Layer U-Net, Vanilla U-Net, Attention U-Net, and state-of-the-art methods. The results of the proposed model are calc... by Muhammad Zubair (728141)

    Published 2025
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  17. 17
    ROC curve for the proposed Classification model across AD, MCI, and CN.

    ROC curve for the proposed Classification model across AD, MCI, and CN. by Muhammad Zubair (728141)

    Published 2025
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  18. 18
    Proposed hybrid framework for Alzheimer’s disease classification.

    Proposed hybrid framework for Alzheimer’s disease classification. by Muhammad Zubair (728141)

    Published 2025
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  19. 19
    Confusion Metrics: Presenting classification results on the test set.

    Confusion Metrics: Presenting classification results on the test set. by Muhammad Zubair (728141)

    Published 2025
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  20. 20
    XAI Saliency map visualization for the proposed model.

    XAI Saliency map visualization for the proposed model. by Muhammad Zubair (728141)

    Published 2025
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