Advisor

Ball, John E.

Committee Member

Tang, Bo

Committee Member

Bruce, Lori

Date of Degree

1-1-2018

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

Comments

Machine Learning||Deep Learning||Convolutional Neural Networks||Computer Vision||Mammography

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