<mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-7.xsd"><mods:titleInfo><mods:title>Using Convolutional Neural Network for Early Breast Cancer Detection</mods:title></mods:titleInfo><mods:typeOfResource authority="primo">dissertations</mods:typeOfResource><mods:name type="personal"><mods:namePart>Rusnak, Anna</mods:namePart><mods:role><mods:roleTerm type="text">creator</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart>Srivastava, Vikas</mods:namePart><mods:role><mods:roleTerm type="text">Advisor</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart>Dawson, Michelle</mods:namePart><mods:role><mods:roleTerm type="text">Reader</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart>Tripathi, Anubhav</mods:namePart><mods:role><mods:roleTerm type="text">Reader</mods:roleTerm></mods:role></mods:name><mods:name type="corporate"><mods:namePart>Brown University. Biology and Medicine: Biomedical Engineering</mods:namePart><mods:role><mods:roleTerm type="text">sponsor</mods:roleTerm></mods:role></mods:name><mods:originInfo><mods:copyrightDate>2022</mods:copyrightDate></mods:originInfo><mods:physicalDescription><mods:extent>iii, 38 p.</mods:extent><mods:digitalOrigin>born digital</mods:digitalOrigin></mods:physicalDescription><mods:note type="thesis">Thesis (Sc. M.)--Brown University, 2022</mods:note><mods:genre authority="aat">theses</mods:genre><mods:abstract>Objective: The objective of this study was to create a convolutional neural network&#13;
(CNN) capable of predicting the size, elastic modulus, and the location of flaws embedded&#13;
in soft materials. One potential application for this methodology is early breast cancer detection.&#13;
This would make breast cancer screening and diagnostic safe for those who cannot&#13;
undergo ionizing radiation and more accessible to those who do not have access to mammography.&#13;
&#13;
Methods: Simulations of a breast containing a tumor of various elastic moduli and&#13;
sizes were created. A pressure load simulating an ultrasound signal emitted from a probe&#13;
was applied to the outside surface of the breast. The resulting signal, along with known&#13;
elastic modulus and size of the tumor, was used to train a convolutional neural network.&#13;
Convolutional neural networks consist of convolution layers where the input data is being&#13;
transform by element-wise multiplication with convolution kernel, pooling layers that down&#13;
sample the resulting data, and fully connected (FC) layers that connect the input and output.&#13;
To experimentally validate the model, tissue mimicking phantoms will be created and imaged&#13;
using an Olympus-1 ultrasound machine.&#13;
&#13;
Results: MAPE for the training data set was 0.733 and 5.66 for size and elastic modulus,&#13;
respectively. MAPE for the testing data set was 1.11 and 7.83 for size and elastic modulus,&#13;
respectively.&#13;
&#13;
Conclusions: The convolutional neural network trained on simulation data was evaluated&#13;
to be highly effective and accurate. Future work is required to experimentally validate&#13;
and expand the model to be able to characterize smaller tumors and tumor density.</mods:abstract><mods:subject authority="fast" authorityURI="http://id.worldcat.org/fast" valueURI="http://id.worldcat.org/fast/00817247"><mods:topic>Artificial intelligence</mods:topic></mods:subject><mods:subject authority="fast" authorityURI="http://id.worldcat.org/fast" valueURI="http://id.worldcat.org/fast/01715717"><mods:topic>Diagnostic ultrasonic imaging</mods:topic></mods:subject><mods:subject authority="fast" authorityURI="http://id.worldcat.org/fast" valueURI="http://id.worldcat.org/fast/01775421"><mods:topic>Abaqus (Electronic resource)</mods:topic></mods:subject><mods:language><mods:languageTerm authority="iso639-2b">English</mods:languageTerm></mods:language><mods:recordInfo><mods:recordContentSource authority="marcorg">RPB</mods:recordContentSource><mods:recordCreationDate encoding="iso8601">20220706</mods:recordCreationDate></mods:recordInfo></mods:mods>