Exploring Transfer Learning for Deep Learning Polyp Detection in Colonoscopy Images Using YOLOv8
Journal:
arXiv
Published Date:
Jan 31, 2025
Abstract
Deep learning methods have demonstrated strong performance in objection
tasks; however, their ability to learn domain-specific applications with
limited training data remains a significant challenge. Transfer learning
techniques address this issue by leveraging knowledge from pre-training on
related datasets, enabling faster and more efficient learning for new tasks.
Finding the right dataset for pre-training can play a critical role in
determining the success of transfer learning and overall model performance. In
this paper, we investigate the impact of pre-training a YOLOv8n model on seven
distinct datasets, evaluating their effectiveness when transferred to the task
of polyp detection. We compare whether large, general-purpose datasets with
diverse objects outperform niche datasets with characteristics similar to
polyps. In addition, we assess the influence of the size of the dataset on the
efficacy of transfer learning. Experiments on the polyp datasets show that
models pre-trained on relevant datasets consistently outperform those trained
from scratch, highlighting the benefit of pre-training on datasets with shared
domain-specific features.
