Diagnosing Breast Cancer with a Neural Network

John Cullen, University of South Florida

Publication Year

2017

Abstract

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.

Recommended Citation

Cullen, John (2017) "Diagnosing Breast Cancer with a Neural Network," Undergraduate Journal of Mathematical Modeling: One + Two: Vol. 7: Iss. 2, Article 4.
DOI: http://doi.org/10.5038/2326-3652.7.2.4880
Available at: https://digitalcommons.usf.edu/ujmm/vol7/iss2/4