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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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. |
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Electrical and Computer Engineering |
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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 |