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Mass Classification of Digital Mammograms Using Convolutional Neural Networks

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.

Identiferoai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-4013
Date04 May 2018
CreatorsFranklin, Elijah
PublisherScholars Junction
Source SetsMississippi State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses and Dissertations

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