Fine needle aspiration (FNA) is a minimally invasive biopsy technique that can be used to successfully diagnose types of cancer, including breast cancer. Immediately, it is difficult for a human to spot any trends in the cell level data gathered during a fine needle aspiration procedure. One way to predict the type of tumor a patient has, is to use a computer to develop a mathematical model based on known data. This project utilizes the Diagnostic Wisconsin Breast Cancer Database (DWBCDB) to create an accurate mathematical model that predicts the type of a patient’s tumor (Malignant or Benign). A neural network model is created in a two step-process. It is first created with random parameters, and is then refined using the data set, with known tumor types. A model with a success rate of 98% is created, which suggests that there is a high level of correlation between FNA data and the type of tumor a patient had. This approach was not capable of producing a perfect model that could be used in clinical applications.
"Diagnosing Breast Cancer with a Neural Network,"
Undergraduate Journal of Mathematical Modeling: One + Two:
2, Article 4.
DOI: http://doi.org/10.5038/2326-36188.8.131.5280 Available at: http://scholarcommons.usf.edu/ujmm/vol7/iss2/4
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Arcadii Grinshpan, Mathematics and Statistics
John Cullen Sr., Principal Software Engineer, Mach7 Technologies
John Cullen Sr.