Search alternatives:
processing optimization » process optimization (Expand Search), process optimisation (Expand Search), routing optimization (Expand Search)
de optimization » d optimization (Expand Search), dose optimization (Expand Search), yet optimization (Expand Search)
primary image » primary stage (Expand Search), primary immune (Expand Search), primary care (Expand Search)
dietary data » history data (Expand Search)
data de » data due (Expand Search)
processing optimization » process optimization (Expand Search), process optimisation (Expand Search), routing optimization (Expand Search)
de optimization » d optimization (Expand Search), dose optimization (Expand Search), yet optimization (Expand Search)
primary image » primary stage (Expand Search), primary immune (Expand Search), primary care (Expand Search)
dietary data » history data (Expand Search)
data de » data due (Expand Search)
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Features selected by optimization algorithms.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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Hybrid feature selection algorithm of CSCO-ROA.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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Sample images from the BreakHis dataset.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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Performance metrics for BrC.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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Proposed CVAE model.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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Proposed methodology.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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Loss vs. Epoch.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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Accuracy vs. Epoch.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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Segmentation results of the proposed model.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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S1 Dataset -
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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CSCO’s flowchart.
Published 2024“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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