/Brest-Cancer-Detection

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Wisconsin Diagnostic Breast Cancer (WDBC)

Source Information

Creators

  • Dr. William H. Wolberg, General Surgery Dept., University of Wisconsin, Clinical Sciences Center, Madison, WI 53792 wolberg@eagle.surgery.wisc.edu

  • W. Nick Street, Computer Sciences Dept., University of Wisconsin, 1210 West Dayton St., Madison, WI 53706 street@cs.wisc.edu 608-262-6619

  • Olvi L. Mangasarian, Computer Sciences Dept., University of Wisconsin, 1210 West Dayton St., Madison, WI 53706 olvi@cs.wisc.edu

Donor: Nick Street Date: November 1995

Relevant information

  • Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at http://www.cs.wisc.edu/~street/images/

  • Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.

    The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server (Needs UWCS lD):

ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/

Dataset Information

Number of instances: 569 Number of attributes: 32 (ID, diagnosis, 30 real-valued input features) Missing attribute values: none Class distribution: 357 benign, 212 malignantpapers

Attribute information

  • ID number
  • Diagnosis (M = malignant, B = benign)
  • Ten real-valued features are computed for each cell nucleus (x3 for mean, *SE *and worst):
    • radius (mean of distances froim center to points on the perimeter)
    • texture (standard deviation of gray-scale values)
    • perimeter
    • area
    • smoothness (local variation in radius lengths)
    • compactness (perimeter^2 / area - 1.0)
    • concavity (severity of concave portions of the contour)
    • concave points (number of concave portions of the contour)
    • symmetry
    • fractal dimension ("coastline approximation" - 1)

Note: The mean, standard error, and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features.
For instance,

  • Field 3 : Mean Radius
  • Field 13 : SE of Radius
  • Field 23 : Worst Radius

All feature values are recoded with four significant digits.

Previous Usage

  • W.N. Street, W.H. Wolberg and O.L. Mangasarian Nuclear feature extraction for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861-870, San Jose, CA, 1993.

  • O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995.

Medical literature

  • W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques to diagnose breast cancer from fine-needle aspirates.
    Cancer Letters 77 (1994) 163-171.

  • W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Image analysis and machine learning applied to breast cancer diagnosis and prognosis.
    Analytical and Quantitative Cytology and Histology, Vol. 17 No. 2, pages 77-87, April 1995.

  • W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computerized breast cancer diagnosis and prognosis from fine needle aspirates.
    Archives of Surgery 1995;130:511-516.

  • W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computer-derived nuclear features distinguish malignant from benign breast cytology.
    Human Pathology, 26:792--796, 1995.

See also:

http://www.cs.wisc.edu/~olvi/uwmp/mpml.html http://www.cs.wisc.edu/~olvi/uwmp/cancer.html

Results from above methods

  • predicting field 2, diagnosis: B = benign, M = malignant
  • sets are linearly separable using all 30 input features
  • best predictive accuracy obtained using one separating plane in the 3-D space of Worst Area, Worst Smoothness and Mean Texture.
  • Estimated accuracy 97.5% using repeated 10-fold crossvalidations.
  • Classifier has correctly diagnosed 176 consecutive new patients as of November 1995.