Date of Graduation

5-2021

Document Type

Thesis

Degree Name

Bachelor of Science in Biomedical Engineering

Degree Level

Undergraduate

Department

Biomedical Engineering

Advisor/Mentor

Quinn, Kyle

Abstract

With deep learning being leveraged more regularly in the field of image classification, particularly in medical imaging, network optimizations have become a field in and of itself. With open source, comprehensive medical image datasets few and far, computational dataset expansion has become a useful tool for researchers without the ability to further manually collect data. However, with the rich amount of data that imaging modalities like multi-photon microscopy collect at a time, there is potential to expand datasets through proper utilization of this data that often time goes unused. Previous deep learning studies have shown that improper expansion can conflate network accuracies and incorrectly train the network. Through the metrics of mean-squared error and correlation coefficient, I want to explore the expansion of image datasets while avoiding this phenomenon that arises from not using independent data.

Keywords

Deep Learning, Bioengineering, Image Classification, Neural Network, Image Expansion, Computer Science, Service Learning

Available for download on Monday, April 22, 2024

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