Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Ball, John E.
Committee Member
Tang, Bo
Committee Member
Bruce, Lori
Date of Degree
5-4-2018
Document Type
Graduate Thesis - Open Access
Major
Electrical and Computer Engineering
Degree Name
Master of Science
College
James Worth Bagley College of Engineering
Department
Department of Electrical and Computer Engineering
Abstract
This thesis explores the current deep learning (DL) approaches to computer aided diagnosis (CAD) of digital mammographic images and presents two novel designs for overcoming current obstacles endemic to the field, using convolutional neural networks (CNNs). The first method employed utilizes Bayesian statistics to perform decision level fusion from multiple images of an individual. The second method utilizes a new data pre-processing scheme to artificially expand the limited available training data and reduce model overitting.
URI
https://hdl.handle.net/11668/17612
Recommended Citation
Franklin, Elijah, "Mass Classification of Digital Mammograms Using Convolutional Neural Networks" (2018). Theses and Dissertations. 3014.
https://scholarsjunction.msstate.edu/td/3014
Comments
Machine Learning||Deep Learning||Convolutional Neural Networks||Computer Vision||Mammography