Mass Classification Using Convolutional Neural Networks

<p>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 utilize...

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Bibliographic Details
Main Author:	Franklin, Elijah
Other Authors:	Bo Tang
Format:	Others
Language:	en
Published:	MSSTATE 2018
Subjects:	Electrical and Computer Engineering
Online Access:	http://sun.library.msstate.edu/ETD-db/theses/available/etd-03212018-153216/

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spelling	ndltd-MSSTATE-oai-library.msstate.edu-etd-03212018-1532162019-05-15T18:44:00Z Mass Classification Using Convolutional Neural Networks Franklin, Elijah Electrical and Computer Engineering <p>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 over-fitting.</p> Bo Tang Lori Bruce John Ball MSSTATE 2018-05-07 text application/pdf http://sun.library.msstate.edu/ETD-db/theses/available/etd-03212018-153216/ http://sun.library.msstate.edu/ETD-db/theses/available/etd-03212018-153216/ en restricted I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, Dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Mississippi State University Libraries or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, Dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, Dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, Dissertation, or project report.
collection	NDLTD
language	en
format	Others
sources	NDLTD
topic	Electrical and Computer Engineering
spellingShingle	Electrical and Computer Engineering Franklin, Elijah Mass Classification Using Convolutional Neural Networks
description	<p>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 over-fitting.</p>
author2	Bo Tang
author_facet	Bo Tang Franklin, Elijah
author	Franklin, Elijah
author_sort	Franklin, Elijah
title	Mass Classification Using Convolutional Neural Networks
title_short	Mass Classification Using Convolutional Neural Networks
title_full	Mass Classification Using Convolutional Neural Networks
title_fullStr	Mass Classification Using Convolutional Neural Networks
title_full_unstemmed	Mass Classification Using Convolutional Neural Networks
title_sort	mass classification using convolutional neural networks
publisher	MSSTATE
publishDate	2018
url	http://sun.library.msstate.edu/ETD-db/theses/available/etd-03212018-153216/
work_keys_str_mv	AT franklinelijah massclassificationusingconvolutionalneuralnetworks
_version_	1719086040388468736

Mass Classification Using Convolutional Neural Networks

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