A deep convolutional neural network approach for biomedical applications.

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2022

Author dc:creator

• Nguyen, Hanh Hong, 1991-

Advisor dc:contributor.advisor

• Schubert, Keith Evan.

• Convolutional neural network (CNN)
• Biomedical.
• Colon.
• Particle.

Statement 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.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/12400

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/12400