Breast Cancer Classification of Mammographic Masses Using Circularity Max Metric, A New Method

<p>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.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Electrical Engineering and Computer Science

Year dc:date.available

2016

Author dc:creator

• Heo, Tae Keun

Contributors dc:contributor

• Sung Shin

• Breast cancer
• classification
• MRI
• Biomedical
• Computer Engineering
• Computer Sciences

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

en

Repository record dc:identifier

https://openprairie.sdstate.edu/etd/1113

OAI identifier oai:identifier

oai:openprairie.sdstate.edu:etd-2108