How many features are extracted per cell in the Diagnostic Wisconsin Breast Cancer Database?
The Diagnostic Wisconsin Breast Cancer Database (DWBCD) is a widely used dataset in the field of medical research and machine learning. It contains various features extracted from digitized images of fine needle aspirates (FNAs) of breast masses, which can be used to classify these masses as either benign or malignant. In the context of building a deep neural network with TensorFlow in Colab, it is important to understand the number of features extracted per cell in this dataset.
In the DWBCD, a total of 30 features are extracted per cell. These features are computed from a digitized image of an FNA and include various characteristics that can provide valuable information for diagnosing breast cancer. Some of the features include:
1. Radius: The average distance from the center to points on the perimeter of the cell.
2. Texture: Standard deviation of gray-scale values in the image.
3. Perimeter: The total length of the cell's perimeter.
4. Area: The area occupied by the cell.
5. Smoothness: Local variation in radius lengths.
6. Compactness: Perimeter^2 / Area – 1.0.
7. Concavity: Severity of concave portions of the contour.
8. Concave points: Number of concave portions of the contour.
9. Symmetry: Symmetry of cell shape.
10. Fractal dimension: "Coastline approximation" – 1.
These features provide important information about the shape, texture, and other characteristics of the cells in the breast mass. By utilizing these features, machine learning algorithms can learn patterns and make predictions about the nature of the mass, whether it is benign or malignant.
It is worth noting that the DWBCD is a relatively small dataset with 569 instances, which may limit the complexity of the models that can be built using this dataset. However, it serves as a valuable resource for understanding and experimenting with deep neural networks using TensorFlow in Colab.
The DWBCD contains 30 features extracted per cell. These features provide valuable information about the characteristics of breast mass cells and can be used to build deep neural networks for breast cancer classification.
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