Document Type
Thesis - Open Access
Award Date
2016
Degree Name
Master of Science (MS)
Department / School
Electrical Engineering and Computer Science
First Advisor
Sung Shin
Keywords
Breast cancer, classification, MRI
Abstract
Breast cancer classification can be divided into two categories. The first category is a benign tumor, and the other is a malignant tumor. The main purpose of breast cancer classification is to classify abnormalities into benign or malignant classes and thus help physicians with further analysis by minimizing potential errors that can be made by fatigued or inexperienced physicians. This paper proposes a new shape metric based on the area ratio of a circle to classify mammographic images into benign and malignant class. Support Vector Machine is used as a machine learning tool for training and classification purposes. The improved performance of the proposed shape metric was used to evaluate and to compare the performances between existing method, which is called Circularity Range Ratio and proposed method, which is called Circularity Max. The result shows that the proposed Circularity Max method improves the Matthews Correlation Coefficient, specificity, sensitivity and accuracy. Therefore, the shape metric can be a promising tool to provide preliminary decision support information to physicians for further diagnosis.
Library of Congress Subject Headings
Breast -- Radiography.
Breast -- Imaging.
Breast -- Cancer -- Diagnosis.
Image processing -- Digital techniques.
Image analysis.
Radiography, Medical -- Digital techniques.
Description
Includes bibliographical references (pages 49-53)
Format
application/pdf
Number of Pages
61
Publisher
South Dakota State University
Recommended Citation
Heo, Tae Keun, "Breast Cancer Classification of Mammographic Masses Using Circularity Max Metric, A New Method" (2016). Electronic Theses and Dissertations. 1113.
https://openprairie.sdstate.edu/etd/1113
Rights Statement
