{"id":{"repo_id":"baylor","oai_identifier":"oai:baylor-ir.tdl.org:2104/12400"},"canonical_url":"https://search.dev.ndltd.org/etd/baylor/oai:baylor-ir.tdl.org:2104/12400","repository":{"repo_id":"baylor","name":"Baylor University","base_url":"https://baylor-ir.tdl.org/server/oai/request"},"display":{"title":"A deep convolutional neural network approach for biomedical applications.","abstract":"Deep learning is a subset of machine learning that uses multi layer neural networks to perform desired tasks by using trained models. Neural networks are nonlinear mapping systems whose structure and function are loosely modeled on the physical structure of the nervous systems in humans and animals. In deep learning, convolutional neural networks (CNNs) have been used to analyze visual tasks for more than 40 years. Since the mid-2000s, they have revolutionized image processing and analysis. The goal of this dissertation is designing a deep CNN approach for biomedical applications, including automation of the process of colon polyps classification as well as single particle identification in radiation therapy.","abstract_html":"Deep learning is a subset of machine learning that uses multi layer neural networks to perform desired tasks by using trained models. Neural networks are nonlinear mapping systems whose structure and function are loosely modeled on the physical structure of the nervous systems in humans and animals. In deep learning, convolutional neural networks (CNNs) have been used to analyze visual tasks for more than 40 years. Since the mid-2000s, they have revolutionized image processing and analysis. The goal of this dissertation is designing a deep CNN approach for biomedical applications, including automation of the process of colon polyps classification as well as single particle identification in radiation therapy.","abstract_has_math":false,"creators":["Nguyen, Hanh Hong, 1991-"],"institution":"Baylor University.","degree_name":"Ph.D.","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Schubert, Keith Evan."],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-24T01:07:58Z","subjects":["Convolutional neural network (CNN)","Biomedical.","Colon.","Particle."],"languages":["en"],"rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2104/12400","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Schubert, Keith Evan."]},{"key":"dc:creator","label":"Author","values":["Nguyen, Hanh Hong, 1991-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-09-26T13:52:09Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-09-26T13:52:09Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-12"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Baylor University."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Convolutional neural network (CNN)","Biomedical.","Colon.","Particle."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2104/12400"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Deep learning is a subset of machine learning that uses multi layer neural networks to perform desired tasks by using trained models. Neural networks are nonlinear mapping systems whose structure and function are loosely modeled on the physical structure of the nervous systems in humans and animals. In deep learning, convolutional neural networks (CNNs) have been used to analyze visual tasks for more than 40 years. Since the mid-2000s, they have revolutionized image processing and analysis. The goal of this dissertation is designing a deep CNN approach for biomedical applications, including automation of the process of colon polyps classification as well as single particle identification in radiation therapy."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A deep convolutional neural network approach for biomedical applications."]}]}],"canonical_facts":{"dc:contributor.advisor":["Schubert, Keith Evan."],"dc:creator":["Nguyen, Hanh Hong, 1991-"],"dc:date.accessioned":["2023-09-26T13:52:09Z"],"dc:date.available":["2023-09-26T13:52:09Z"],"dc:date.issued":["2022-12"],"dc:description.abstract":["Deep learning is a subset of machine learning that uses multi layer neural networks to perform desired tasks by using trained models. Neural networks are nonlinear mapping systems whose structure and function are loosely modeled on the physical structure of the nervous systems in humans and animals. In deep learning, convolutional neural networks (CNNs) have been used to analyze visual tasks for more than 40 years. Since the mid-2000s, they have revolutionized image processing and analysis. The goal of this dissertation is designing a deep CNN approach for biomedical applications, including automation of the process of colon polyps classification as well as single particle identification in radiation therapy."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/2104/12400"],"dc:language.iso":["en"],"dc:rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."],"dc:subject":["Convolutional neural network (CNN)","Biomedical.","Colon.","Particle."],"dc:title":["A deep convolutional neural network approach for biomedical applications."],"dc:type":["Thesis"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["Baylor University."]},"updated_at":"2026-07-24T01:07:58Z"}