Imbalanced Classification Model to Detect Mammography Microcalcifications
Last Updated on August 21, 2020
Cancer detection is a popular example of an imbalanced classification problem because there are often significantly more cases of non-cancer than actual cancer.
A standard imbalanced classification dataset is the mammography dataset that involves detecting breast cancer from radiological scans, specifically the presence of clusters of microcalcifications that appear bright on a mammogram. This dataset was constructed by scanning the images, segmenting them into candidate objects, and using computer vision techniques to describe each candidate object.
It is a popular dataset for imbalanced classification because of the severe class imbalance, specifically where 98 percent of candidate microcalcifications are not cancer and only 2 percent were labeled as cancer by an experienced radiographer.
In this tutorial, you will discover how to develop and evaluate models for the imbalanced mammography cancer classification dataset.
After completing this tutorial, you will know:
- How to load and explore the dataset and generate ideas for data preparation and model selection.
- How to evaluate a suite of machine learning models and improve their performance with data cost-sensitive techniques.
- How to fit a final model and use it to predict class labels for specific cases.
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