Comparative Analysis of Transfer Learning Strategies for Polyp Detection in Colonoscopy Images Using YOLOv8

Author

Fabian Vazquez Jr., The University of Texas Rio Grande Valley

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

Dongchul Kim

Abstract

The early detection of polyps during colonoscopy procedures is crucial for preventing colorectal cancer, a leading cause of cancer-related deaths globally. Traditional methods for polyp detection are often time-consuming and prone to human error. This thesis investigates the effectiveness of transfer learning, the process of taking a pre-trained model that was trained on a large dataset and adapting it to a new, but related task, requiring less data and time for training. This research compares whether the YOLOv8 model trained from scratch on a specific polyp dataset is outperformed by transfer learning methods such as utilizing a pretrained model on general object detection or a pretrained model on similar medical images of other internal human body structures, which are then fine-tuned specifically for polyp detection. Each of these strategies will be applied to the YOLOv8 model and evaluated on a comprehensive dataset of colonoscopy images with standard evaluation metrics, including precision, recall, F1-score, and mean Average Precision (mAP). This study provides valuable insights into optimizing transfer learning methods for medical imaging tasks, offering a robust framework for improving the accuracy of polyp detection with more efficient training methods that require less time and data.

Comments

Copyright 2024 Fabian Vazquez, Jr. https://proquest.com/docview/3115717988

Recommended Citation

Vazquez, Fabian Jr., "Comparative Analysis of Transfer Learning Strategies for Polyp Detection in Colonoscopy Images Using YOLOv8" (2024). Theses and Dissertations. 1556.
https://scholarworks.utrgv.edu/etd/1556