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Transfer_Learning_for_Image_Cancer_Cell_Classification

Life ScienceImage AnalysisDeep LearningKeras
kathrin profile image
VersionUpdated to 5.4.0Latest, created on 
Feb 25, 2025 7:28 AM
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Transfer Learning for Cancer Cell Classification

When running this workflow finish executing each section before starting the next. Each portion of the workflow creates files referenced by the next.

Note: Download the dataset from Kaggle (https://www.kaggle.com/datasets/andrewmvd/malignant-lymphoma-classification) and save the zip file in the _data/downloads folder relative to your current workflow.


1) Unzip the downloaded file and assign class labels: This section unzips the file and assigns class label to each image.

2) Preprocessing: this section reads all the images into a knime format and chops them up into patches that will be fed into our model later.

3) Train Model: the final section reads VGG16, adds layers, trains, and scores our new model.

External resources

  • VGG16 CNN
  • Dataset file
  • Dataset information
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Used extensions & nodes

Created with KNIME Analytics Platform version 5.4.2
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    KNIME Base nodesTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 5.4.1

    knime
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    KNIME Deep Learning - Keras IntegrationTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 5.4.0

    knime
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    KNIME ExpressionsTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 5.4.1

    knime
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    KNIME Image ProcessingTrusted extension

    University of Konstanz / KNIME

    Version 1.8.3

    bioml-konstanz

Legal

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