Theses and Dissertations

Date of Award

8-1-2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Bin Fu

Second Advisor

Emmet Tomai

Third Advisor

Tamer Oraby

Abstract

Colorectal cancer is among the deadliest cancers, but fortunately, this type of cancer can be prevented. The best current method of prevention is via detecting the bad polyps in the colon in time. Furthermore, the best method we have available to detect these bad polyps is through colonoscopies. Even though a lot of lives have been saved via these methods, it is still not perfect because of human error. Integrating artificial intelligence into colonoscopy procedures is our next evolution in increasing our prevention of colorectal cancer. Polyp detectors are one of the tools brought by advancements in technology that may aid doctors in colonoscopy procedures. It is vital that we continually improve the quality of polyp detectors. A common problem in polyp detectors is that they sometimes confuse the white light specular reflections produced by the endoscope with polyps. This study proposes a new data augmentation technique that artificially augments the images with more white light specular reflections. The hypothesis is that by providing the model more opportunities to make mistakes, it also gives it more chances to learn from those mistakes, thus improving the quality of the model.

Comments

Copyright 2024 Jose Angel Nuñez. https://proquest.com/docview/3116151041

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