Theses and Dissertations
Date of Award
8-2016
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Dr. Laura Grabowski
Second Advisor
Dr. Emmett Tomai
Third Advisor
Dr. Dongchul Kim
Abstract
This thesis examines how Artificial Neural Networks can be used to classify a set of samples from a fine needle aspirate dataset. The dataset is composed of various different attributes, each of which are used to come to the conclusion as to whether a sample is benign or malignant. To automate the process of analyzing the various attributes and coming to a correct prediction, a neural network was implemented. First, a Feedforward Neural Network was trained with the dataset using a Backpropagation training method and an activation sigmoid function with one hidden layer in the architecture of the network. After training, the network performed a 10-fold cross validation to determine which model had the lower error scores and would perform the best on the data. The data was looped through the model and the trained network classified the samples as either benign or malignant. Once classified, the overall accuracy, specificity and sensitivity were analyzed to measure performance. Three other neural networks were compared to the Feedforward Network to see how they performed. These three neural networks included a NEAT Neural Network, a Support Vector Machine, and a Radial Basis Function Neural Network.
Recommended Citation
Vazquez, Janette, "Analysis of artificial neural networks in the diagnosing of breast cancer using fine needle aspirates" (2016). Theses and Dissertations. 163.
https://scholarworks.utrgv.edu/etd/163
Comments
Copyright 2016 Janette Vazquez. All Rights Reserved.
https://www.proquest.com/dissertations-theses/analysis-artificial-neural-networks-diagnosing/docview/1850204692/se-2?accountid=7119