Search alternatives:
process optimization » model optimization (Expand Search)
art optimization » swarm optimization (Expand Search), after optimization (Expand Search), path optimization (Expand Search)
image process » damage process (Expand Search), image processing (Expand Search), simple process (Expand Search)
binary model » final model (Expand Search), injury model (Expand Search), tiny model (Expand Search)
model art » modern art (Expand Search), model care (Expand Search), model a (Expand Search)
process optimization » model optimization (Expand Search)
art optimization » swarm optimization (Expand Search), after optimization (Expand Search), path optimization (Expand Search)
image process » damage process (Expand Search), image processing (Expand Search), simple process (Expand Search)
binary model » final model (Expand Search), injury model (Expand Search), tiny model (Expand Search)
model art » modern art (Expand Search), model care (Expand Search), model a (Expand Search)
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Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm
Published 2025“…This strategy </p><p dir="ltr">not only improves detection efficiency and accuracy but also supports early diagnosis and treatment planning, </p><p dir="ltr">leading to better patient outcomes. By leveraging the binary GWO algorithm to optimize the feature selection </p><p dir="ltr">process and CNNs for image classification, the proposed approach reduces computational costs while increasing </p><p dir="ltr">classification accuracy. …”
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Image processing workflow.
Published 2020“…<p>Raw fluorescent microscope images (a) were processed with a binary segmentation algorithm, and clusters of bacterial cells were manually annotated. …”
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The comparison of the accuracy score of the benchmark and the proposed models.
Published 2025Subjects: -
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A* Path-Finding Algorithm to Determine Cell Connections
Published 2025“…</p><p dir="ltr">Astrocytes were dissociated from E18 mouse cortical tissue, and image data were processed using a Cellpose 2.0 model to mask nuclei. …”
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Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).
Published 2025Subjects: -
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Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction.
Published 2025Subjects: -
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ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.
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The statistical description of the original data set of the patients (<i>n</i> = 162).
Published 2025Subjects: -
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The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
Published 2025Subjects: -
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