A deep convolutional neural network approach for biomedical applications.
| 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.created | 2022-12 | |
| dc.date.issued | December 2022 | |
| dc.date.submitted | December 2022 | |
| dc.date.updated | 2023-09-26T13:52:09Z | |
| 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 | ||
| dc.identifier.uri | https://hdl.handle.net/2104/12400 | |
| dc.language.iso | English | |
| dc.rights.accessrights | Worldwide access | |
| dc.title | A deep convolutional neural network approach for biomedical applications. | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Baylor University. Dept. of Electrical & Computer Engineering. | |
| thesis.degree.grantor | Baylor University | |
| thesis.degree.name | Ph.D. | |
| thesis.degree.program | Electrical & Computer Engineering | |
| thesis.degree.school | Baylor University |
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